From d721f51231e799761fb0b610fa5c532ee6182da0 Mon Sep 17 00:00:00 2001 From: Zhen-Zhang Date: Fri, 16 Feb 2024 20:59:29 +0800 Subject: [PATCH] Init submit --- .DS_Store | Bin 0 -> 6148 bytes README.md | 32 ++- data/Citation.zip | Bin 0 -> 5679897 bytes datasets.py | 271 +++++++++++++++++++++++ fig/model.png | Bin 0 -> 211371 bytes layer.py | 536 ++++++++++++++++++++++++++++++++++++++++++++++ model.py | 40 ++++ train_source.py | 133 ++++++++++++ train_target.py | 312 +++++++++++++++++++++++++++ utils.py | 132 ++++++++++++ 10 files changed, 1455 insertions(+), 1 deletion(-) create mode 100644 .DS_Store create mode 100644 data/Citation.zip create mode 100644 datasets.py create mode 100644 fig/model.png create mode 100644 layer.py create mode 100644 model.py create mode 100644 train_source.py create mode 100644 train_target.py create mode 100644 utils.py diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..baf11bac0dcef80181c27ea0725e444545a6417f GIT binary patch literal 6148 zcmeHKJ&)5c7=GOr&ZPo6aMCTHNUT+9DMC82^m;uc29^tg0Z>Vr(u+vrDoLqMRZ(YH z_!SKN0v5!?z`_PU2YCEZk?UO;5kh_?`|-#7*zxl^aZE(4Hw)TCbt2+O5-S&wonf+H zrc+ijEth~yj&Uy%Lq8He>T*D9RspNPzpa3LcPkW-M;Un=@9#If)*pW=Q;Vpr{V1go z{8MVul#b6}O>(R9lExyHT{Hc2+=5FR?v$W?W zk7ezF5Rtzwd;Y_4G#}Kj?29<_!g!P@LKuwT^5jt%2V&k6vp7f<*VP@o%BzF=&SKGN zcN$Koy}N8Ui`|>8hI6ZPd%3Lg?dv!091dPYlUTggp#(OE-xl5sxvFd^%1MB6f@@B!w}!nu`Ia z2?<1R1YSYR=xffuN{4hnd(@}q$Sv9NzRspNPh63{Y;2=rt8eC~q zpAKZ|3IMF2SsBXe*B|J}24L6VN+VieOjCiH%FGpmnVSRCbo6(P|4O5#6H_x|95XXB zHxy7jNAV7lGW0pJ0PGrEX+#Um{s<@;Y-Sbss|x%A`0&-3 literal 0 HcmV?d00001 diff --git a/README.md b/README.md index f00756e..54ce413 100644 --- a/README.md +++ b/README.md @@ -1 +1,31 @@ -# GraphCTA \ No newline at end of file +# GraphCTA +Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation (WWW 2024) + +![](https://github.com/cszhangzhen/GraphCTA/blob/main/fig/model.png) + +This is a PyTorch implementation of the GraphCTA algorithm, which tries to address the domain adaptation problem without accessing the labelled source graph. It performs model adaptation and graph adaptation collaboratively through a series of procedures: (1) conduct model adaptation based on node's neighborhood predictions in target graph considering both local and global information; (2) perform graph adaptation by updating graph structure and node attributes via neighborhood constrastive learning; and (3) the updated graph serves as an input to facilitate the subsequent iteration of model adaptation, thereby establishing a collaborative loop between model adaptation and graph adaptation. + + +## Requirements +* python3.8 +* pytorch==2.0.0 +* torch-scatter==2.1.1+pt20cu118 +* torch-sparse==0.6.17+pt20cu118 +* torch-cluster==1.6.1+pt20cu118 +* torch-geometric==2.3.1 +* numpy==1.24.3 +* scipy==1.10.1 +* tqdm==4.65.0 + +## Datasets +Datasets used in the paper are all publicly available datasets. You can find [Elliptic](https://www.kaggle.com/datasets/ellipticco/elliptic-data-set), [Twitch](https://github.com/benedekrozemberczki/datasets#twitch-social-networks) and [Citation](https://github.com/yuntaodu/ASN/tree/main/data) via the links. + +## Quick Start: +Just execuate the following command for source model pre-training: +``` +python train_source.py +``` +Then, execuate the following command for adaptation: +``` +python train_target.py +``` diff --git a/data/Citation.zip b/data/Citation.zip new file mode 100644 index 0000000000000000000000000000000000000000..967ba4e52b281bbed5119092f9b6640995f889d8 GIT binary patch literal 5679897 zcmd?Q2UL?=w>G*$C?ZG^3mpUzMWscWR0UygQHm&tNRbj0kQSwvU;`8>vIRmHR1oPA z5NUCX6bS?+(u7bAod^ja{a^8X-#Po+=bXQc@sE4&7&l{JkidG^JJ&4Fe4hE586#Qu z0Okvm8*cgQn}7Vn2_%93ja%1l-SF~MxO8zB;Hde0$azPTi!<~6b^sSZur470`1R*z zzn0kncKpjS18^w;xQxC1`BVC)7MB&|?f<#rzbq&T7ya9UP7ZsWTpb)8ew;sN`GeOo z$_&dAFNl=o6G1G?c%jFP@ED#dk=kd>Yanx~Cre83D|UId*JW1H3KVA9IiYwtMD%|J?|De>H-^ z>2qd&YX4yirXBvLw)l!E|96wJ3IeeEudB%$SlC&dm3}nPM*4 z-o49AuxY5v<=fPQV|VWCy@##(sDe57NbSZx1y&bF1BI7l=Oea??VLB?UH#+q`}eGM zE1yW&Rg}OH*Iq*0aQ&U^Rk~lM^KlC=ONN&B&x+8j#Pqz8^h6{uplQlsxxpUgv&M?% zd%hic8oz(c@QXW{7U-wusDb8=?g#4GJ+r}A;N@1Gp9DVARg$}7z!ET5hVEwa4S?d8cG z=@PWMHl1PAo^03b>Qx?d&!Ly={tqvDLT#nne5pzNAI0@ME$Lr*P~6<-8p1R~8p1bT znBP)&Rlj4q#UsHLK9$+Gq)n|lU~6*i$kyO}zQVWF1VWs)+Xy1`uvT_-=+CQ9izct9 z#+km|p1-cOb;NF>sxD;uQ}+Zbmjjk(W#rn`hNmQh;=Rh>YeSNr-la8vJef6;9jaV> zr&*)xLtNnZie)Q|d-wgWJMvjH#e4Veq0L&?vL#i<4@iql;IX*XrHB{#L zz30CPz54Z${1sMS30B&2S1#+rLMu3 zexbZa&L>!D9wgeccBXlJv>HyfI_1`4Znz*NdGMCq8Yk1GW7>Z#%CNwQgs%1<0r^)a z41g{!_Sb;?XE?&h{LlW#49owc&@2<-yZdvm9uZiKND{C}^#}J@PvQ0|+DC`xv`?~sX_F;aAU=x|`~hP`v>g$+l|vOV9x#UNZ9VwYru|B2JGoNespd}cGM z``%tJW?LM6Q*-=Uvo}WV^nJ`9DUo!wOk$f$MQrVW*sZ)Im!$TGzSPqz9~au^X1Ycy zowwSZZ|_^)m^^V^S@!MD<`Y+6@JgtRsAraZ>;Bfi@uoc`Ktjc?6 zima!4PYrCp?@TJL+dCtDk|*=?n`g&ugO5_BPQDbl()FP7)?9m5(&&hOnd?V^hhN@2 zn~Srv=G)qZRG91D&YhbGKgGADDA#ew`r)!(X@rgYd5f1c-eI~ScEu4rc%fEeK<&*7 zcdx!3J5JlzBA6bd>n}BV4Dol5{_Bx2ed!=Pl7BBT|MVDzf1b_X9GiJ6|IawK+20DP z@PFYs-o9Q=E*Ol9^S^my0IVpL@QeBWH`n=(fcg8n{}M3&xNF~Q{{NdR{#QQEOcDQX z$A5kUL-0TQIn$c|t)K6C5S}OWMe0D@xt60&UJeCJNAG-|qyO6AOg@)B`~07r@#=tM zvRmJq({V!}CN-x#ekWzWb(XWP?k`6tPs&TM2t2mIF8IE5W|I{7~x)tL4Zz9T6rIlt|2 z-PnrqaE@P$BlN`0U*G%ii*<1p-O#srw@5WLURZplUOu|(%#HreWuuZ~JGLKWCi|^# zf1F=$klYb2xHoU?);;R!()-`s&)XL3oE)Axg}trhG-w&Jn=I1*tH$_2$u8imFW5<`S-Jd(udo8)M=_co{nzz<|g0m-**^)e8iKR7?%g&{f zBag*@@cf3EJ<0A=`i&$Hp?~C?N+Ki3cj^4VzZ%VVM4S6%0_d(csnp&6`Q!n4Lw*QE6PQEm;7-s0A~!V>RZ4xPKy$#VS7cyn$-dEQ}}O50bl#vT_3 z<&%5oO;Ihs^)Jxo-!}JuBU+gq;6GE8!oMy1e-@~JEqEme z0ObC<;6Do9e{9s-*Tw#p?=?>h+ROK*y!WkN?G>}2{`2c^q099HjlSkEJnTRKEZnEL zqtCPo;apy=9f_8S4r1%m@H7eGHb0ylafBs5>%D@mLPh*31wvZJ-3Twbnoc&~4vygy z8Cmc5cm`R!nfsu_GSddN>C;9k{{Sz-Dl@aG%SiZIH$lptvIKR9S6!5V-so6>vnJ>~YuSzPC&+u=~ov&f~gyVvavcVzJ&Yx>0g`2(vZuQ}JR zDp9rA|1cl=6VnR+f$;xsAKCw$RAj}{F45WPx2R?AK!^z~Evy25|HoVRmdqFFsPQ|i_jbf5K-uvvTTA2x0@6f|?x zK5*uKR=|wx(AqGbw_*LWSHl|4EBwdW-I8#cn(o$;{pPgI_V?(a^~J~%-B3C$Xj`*H zXS2wvM0b7iLrM68SN*2F*LMHh_PfBDyzPnhNvl!@JdluJEsO z+Y=l^flJE`t3SKELVqlTZ;forrW>}x6}GuaYuK6`BMpUZIm)<n3fepn^SA(e(e=L@pWAybaBqA0Yi6fSf?ru z)#(x%wi$`EjS<@RME%dr@OdgJeC74fM#J{lQoDTsy{4Z2-D|7Ie)WBO+R#FV!uBv# zVe`GeSNK%J#u~MxHkfYJ@Uv=rC9T14AWl*=V5tbgEzfPcf)0S zYg1vJ*E75?JYc15E4N3@K757hTL0S@=no5n3Tt>>t=_?sACJ^@S3cN}Owhvb+rQo1 z8@4cS_47rKTEoVCyKWe**^761n%?8z(5M@@XuR>IU3YUtVSzLs9vvRGZoKiR#(vbk zzJApTTI;3R?+s4yJ_K@OG5z@&Gn1(+iOGH%iAHV zMOB{L6KWlP1gosu1k%QA+gkXl70gY1L$u9LlLCYRhNO|XF-%xMYr@qK@I4yj)^lDa z2l&FDTpU?v#~N48a@Kr*bJ!=^muL8d_Lan1s-!6peXkSI%eWrw$UQN|3qU5_kTur% zB>)5qN(*3kGwRD4Kt4g|?mLE>*++9aaVU|)x6|KAJGSBKkMvYvs1zP$pxtrg3MDs# zqt{RYDReR8(j^)jNa$+-fhm3KQ4&UGMgSO0wC@a@8lpF{V+&_XbsB~8|I|W)0Lg{!%AI?+0;20crv) zi>Md}Z`b>=3*}pY`94knd;(u1gi=cF(gb5Pymeq~@H0$nkvo?*rpv{ONqv#!TJ7q*7h|?yqWC29 z4#ye-T>eyZpk%uGb(rqxg{$+a{mRl{d-HR4Z8f<~>}Y zs!t=C*6#a>gYXN)t(vg*HjA=(R-~|@NXa-F4{0iLu5n&Q2^W`+F+C2z=fDy`Q1v@( z{OajglnlVC@#`I}qs|<|gd_#C5S*4JfKizR01gsf(;ITs($Ng85>WuIEGeg3HC#qq zieH^gKa>YRMZge3QF@(Mk_ROu_;CXs`IVF<8>jX<*T?*dCDv5Rv6aHJTiLvI?7(@r zokfoeKc8$(+tqnxX4NeW2}D2C6zFKMW9MegbIcQoo~FE$5&(T|!Y6tS06|h3H#RD0 zR#uY_v~&bN$jz6@f9)EcCgYrKf~Z94M;H^MoQQjjhF)tOGSX9-pr&qe5Ix9O3r_GJ z9c0Vtv;yD_wVe|fjL-(bo1B#hZlavH1#fz;D!BeJ7MECy1QcXXG(Y3T#ASEmcIP5| z;Y0_Ts!j^=$DSo(K=yuGY*!jFG7E>!AFCgK-is$&XRJKI8szutyY5O1^ zY;NCipp?=KfXDaa8WI{DQp#xnP=^PNSf}|$zhnVDcZ$fxrMHz|H9%jl?SKVW;S01R zd);pa)4Ks^X;dTL7-b=7;OC>@G4x!VdMvl{>k`*LlFaEMIQ=D*ghr$f!`@`EyQ8oy zV~O(SMKO%xT32j7Q5Iygd2v??n`=%#^&rn)9_0m_eGSJhy|648K*l~-kkk$32AjP# zdC^?(=2$c;=56r^kdm>&m;O?1@=VeDlR?xY3!uYVS%Mh7l+(cR5TXj5$|f|Q zIucV9jUWV|m4IT}1WkhWOn96h8#Ai_B&EFsmCf>;-@Ng(Ay;Ky+c)91xoqFVksQqn z3Z48}#vSZ*`M4Z*p_m~MRlG)0C>Y|EWWoC#TY%JhF04~Z9vDk`57u!n z3|{I)s1Uhfpxq2HtvFD8GyUP~%Sc>`HB6*>!RX^Ci*1b-0TArcD@76XTU2*4NNeO#BW$u>*^i5>J2-&vt3 znq#>kYx!is1G1Edn*KJ?+o<1M*O6fCc1I{5gB>9HT-Wz%|DDom zP(;@@=wOb!|oW!iyR}&{!4=zEt$AV1;G)$aah&_8k#De4V;8KEj$! 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z40qZK488CI0!)uwYKAo(gO#jgYhd^1->RDeYd2ICP4Rku(3D`wvTCVAN?{+vdT0!! z&_$)CV)1%9;6_!cS|OZBC=^w9@7>CuSU+&FkZxEPbHJXo_&{p*wP zaUprEAf$##++7+FT7)8j3_Ey}%VECY9I!ivbi=N|wuEbh&}0M?bE_EcA{^(!`HRXO z7__-~N0Lwj1iQJZvecc;Cad>9X6ViOZ#~D`2A!^OoCv#^7?d`xj*0W{ z9#texK~QFuOs8=VFlQm95&J1r}9ML zOch-N-~=@6Wx#0Z%-)w7PFTd>!qK#riFz)lJ`r&0)kGV6KZCxfrqJSDC7qtGWU$YQ z<6u*^^V0NS`^J!p6wOuTX&C0}ZW(5ZPX0M_UfVpcAchA@(aNa3emtUSFtKRjz^mx~ zMeoM(FWTr%$&z6Mu1Ym031L_Nm80#kk*S#n3x+;>v%6b)n+%XClkrfQU@s|?3^= n: + continue + features[int(node), np.array(feats, dtype=int)] = 1 + new_label = label[reorder_node_ids] + label = new_label + + return A, label, features + + def process(self): + A, label, features = self.load_twitch(self.name) + edge_index = torch.tensor(np.array(A.nonzero()), dtype=torch.long) + features = np.array(features) + x = torch.from_numpy(features).to(torch.float) + y = torch.from_numpy(label).to(torch.int64) + + data_list = [] + data = Data(edge_index=edge_index, x=x, y=y) + + random_node_indices = np.random.permutation(y.shape[0]) + training_size = int(len(random_node_indices) * 0.8) + val_size = int(len(random_node_indices) * 0.1) + train_node_indices = random_node_indices[:training_size] + val_node_indices = random_node_indices[training_size:training_size + val_size] + test_node_indices = random_node_indices[training_size + val_size:] + + train_masks = torch.zeros([y.shape[0]], dtype=torch.bool) + train_masks[train_node_indices] = 1 + val_masks = torch.zeros([y.shape[0]], dtype=torch.bool) + val_masks[val_node_indices] = 1 + test_masks = torch.zeros([y.shape[0]], dtype=torch.bool) + test_masks[test_node_indices] = 1 + + data.train_mask = train_masks + data.val_mask = val_masks + data.test_mask = test_masks + + if self.pre_transform is not None: + data = self.pre_transform(data) + + data_list.append(data) + + data, slices = self.collate([data]) + + torch.save((data, slices), self.processed_paths[0]) \ No newline at end of file diff --git a/fig/model.png b/fig/model.png new file 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z#863_l@{530H8w9bS(D9@v!WsjZaLIvIMMaq&J9NI;+^+`D>Gmwr ziO-EG4Fv_oV+FTYNAeQQPV^;mRA`awGI$IfSua7jcd@DvZ9|vFyrNKlsG}w-3RM^4 zbd&z?2IU78Qd8HP8M5HwZmCj?h*y0n46fL=XPiC%%8al@C5R_)%L4ajM@fX5ginLc zk;ZtwM+RBRy(0M2vuIS*D398}f$ECT4eZMyW^r$wSMO*_L{#{<@>(}rf4?&rWgQlh z`B_-pY`~n7nt~cZa^UyQJ5m_-gq<57;i0yJg7mtuPo>qdR-GN}Ud%7S74t_wI#O#8 z__dEQ4;mW;{!%(X2J8g@;efGzXKa|+oN#Q^zQD{7rC(j~`BnM*s zh=dte%8fQ+jOH92TW{7#sN3`Y%aK9pA#0goXmzsER`j+jQOq8SNS=^Y+w$Y>wT3EY z_@!5=5jo6;N?LS8B)r6GM$tcrzezT*NpxX=YhK|c14pi-IrW>R2@0S1rmjyn1@x!C zz+mwS2_pHDF({(4S((CWdg7Bnru07{V+aUj(jT2`x7^C5qX}DUvYu<4TC^WR3szDn z%>?rxqC+f`f0W@;QzHy`)v(=^*fRWe58a|U>dQRUwSz-_ed03G(g_H^j4rg7cw>Ya zOi-hV7)3}k#zm+AcQO_MH&E%w1!pyWwA21S((peF=RYLg|Mg3U4_y2!CTSRp25$-Fsz+?XCgee^GBQ2pIUL|T2^nU Tensor: + """""" + if isinstance(x, Tensor): + x: OptPairTensor = (x, x) + + # propagate_type: (x: OptPairTensor) + out = self.propagate(edge_index, x=x, size=size) + + return out + + def message(self, x_j: Tensor) -> Tensor: + return x_j + + def aggregate(self, x: Tensor, index: Tensor, ptr: Optional[Tensor] = None, dim_size: Optional[int] = None) -> Tensor: + return scatter(x, index, dim=self.node_dim, dim_size=dim_size, reduce=self.aggr) + + +class GCN(nn.Module): + def __init__(self, args): + super(GCN, self).__init__() + self.args = args + self.num_features = args.num_features + self.nhid = args.nhid + self.num_classes = args.num_classes + self.dropout_ratio = args.dropout_ratio + self.num_layers = args.num_layers + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + + self.convs.append(GCNConv(self.num_features, self.nhid)) + self.bns.append(nn.BatchNorm1d(self.nhid)) + + for _ in range(self.num_layers - 1): + self.convs.append(GCNConv(self.nhid, self.nhid)) + self.bns.append(nn.BatchNorm1d(self.nhid)) + + self.cls = torch.nn.Linear(self.nhid, self.num_classes) + + self.activation = F.relu + self.use_bn = args.use_bn + + def reset_parameters(self): + for conv in self.convs: + conv.reset_parameters() + for bn in self.bns: + bn.reset_parameters() + + def forward(self, x, edge_index, edge_weight=None): + x = self.feat_bottleneck(x, edge_index, edge_weight) + x = self.feat_classifier(x) + + return x + + def feat_bottleneck(self, x, edge_index, edge_weight=None): + for i, conv in enumerate(self.convs): + x = conv(x, edge_index, edge_weight) + if self.use_bn: + x = self.bns[i](x) + x = self.activation(x) + x = F.dropout(x, p=self.dropout_ratio, training=self.training) + return x + + def feat_classifier(self, x): + x = self.cls(x) + + return x + + +class SAGEConv(MessagePassing): + def __init__(self, in_channels: Union[int, Tuple[int, int]], + out_channels: int, normalize: bool = False, + bias: bool = True, **kwargs): # yapf: disable + kwargs.setdefault('aggr', 'mean') + super(SAGEConv, self).__init__(**kwargs) + + self.in_channels = in_channels + self.out_channels = out_channels + self.normalize = normalize + + if isinstance(in_channels, int): + in_channels = (in_channels, in_channels) + + self.lin_l = Linear(in_channels[0], out_channels, bias=bias) + self.lin_r = Linear(in_channels[1], out_channels, bias=False) + + self.reset_parameters() + + def reset_parameters(self): + self.lin_l.reset_parameters() + self.lin_r.reset_parameters() + + def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj, + size: Size = None) -> Tensor: + """""" + if 0: + if isinstance(x, Tensor): + x: OptPairTensor = (x, x) + # propagate_type: (x: OptPairTensor) + out = self.propagate(edge_index, x=x, size=size) + out = self.lin_l(out) + else: + if isinstance(x, Tensor): + x: OptPairTensor = (x, x) + out = self.lin_l(x[0]) + # propagate_type: (x: OptPairTensor) + out = self.propagate(edge_index, x=(out, out), size=size) + + x_r = x[1] + if x_r is not None: + out += self.lin_r(x_r) + + if self.normalize: + out = F.normalize(out, p=2., dim=-1) + + return out + + def message(self, x_j: Tensor) -> Tensor: + return x_j + + def message_and_aggregate(self, adj_t: SparseTensor, + x: OptPairTensor) -> Tensor: + # Deleted the following line to make propagation differentiable + # adj_t = adj_t.set_value(None, layout=None) + return matmul(adj_t, x[0], reduce=self.aggr) + + def __repr__(self): + return '{}({}, {})'.format(self.__class__.__name__, self.in_channels, + self.out_channels) + + +class SAGE(nn.Module): + def __init__(self, args): + super(SAGE, self).__init__() + self.args = args + self.num_features = args.num_features + self.nhid = args.nhid + self.num_classes = args.num_classes + self.dropout_ratio = args.dropout_ratio + self.num_layers = args.num_layers + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + + self.convs.append(SAGEConv(self.num_features, self.nhid)) + self.bns.append(nn.BatchNorm1d(self.nhid)) + + for _ in range(self.num_layers - 1): + self.convs.append(SAGEConv(self.nhid, self.nhid)) + self.bns.append(nn.BatchNorm1d(self.nhid)) + + self.cls = torch.nn.Linear(self.nhid, self.num_classes) + + self.activation = F.relu + self.use_bn = args.use_bn + + def reset_parameters(self): + for conv in self.convs: + conv.reset_parameters() + for bn in self.bns: + bn.reset_parameters() + + def forward(self, x, edge_index, edge_weight=None): + x = self.feat_bottleneck(x, edge_index, edge_weight) + x = self.feat_classifier(x) + + return x + + def feat_bottleneck(self, x, edge_index, edge_weight=None): + if edge_weight is not None: + adj = SparseTensor.from_edge_index(edge_index, edge_weight, sparse_sizes=2 * x.shape[:1]).t() + + for i, conv in enumerate(self.convs): + if edge_weight is not None: + x = conv(x, adj) + else: + x = conv(x, edge_index, edge_weight) + if self.use_bn: + x = self.bns[i](x) + x = self.activation(x) + x = F.dropout(x, p=self.dropout_ratio, training=self.training) + return x + + def feat_classifier(self, x): + x = self.cls(x) + + return x + + +class GATConv(MessagePassing): + _alpha: OptTensor + + def __init__(self, in_channels: Union[int, Tuple[int, int]], + out_channels: int, heads: int = 1, concat: bool = True, + negative_slope: float = 0.2, dropout: float = 0.0, + add_self_loops: bool = True, bias: bool = True, **kwargs): + kwargs.setdefault('aggr', 'add') + super(GATConv, self).__init__(node_dim=0, **kwargs) + + self.in_channels = in_channels + self.out_channels = out_channels + self.heads = heads + self.concat = concat + self.negative_slope = negative_slope + self.dropout = dropout + self.add_self_loops = add_self_loops + + # In case we are operating in bipartite graphs, we apply separate + # transformations 'lin_src' and 'lin_dst' to source and target nodes: + if isinstance(in_channels, int): + self.lin_src = Linear(in_channels, heads * out_channels, + bias=False, weight_initializer='glorot') + self.lin_dst = self.lin_src + else: + self.lin_src = Linear(in_channels[0], heads * out_channels, False, + weight_initializer='glorot') + self.lin_dst = Linear(in_channels[1], heads * out_channels, False, + weight_initializer='glorot') + + # The learnable parameters to compute attention coefficients: + self.att_src = Parameter(torch.Tensor(1, heads, out_channels)) + self.att_dst = Parameter(torch.Tensor(1, heads, out_channels)) + + if bias and concat: + self.bias = Parameter(torch.Tensor(heads * out_channels)) + elif bias and not concat: + self.bias = Parameter(torch.Tensor(out_channels)) + else: + self.register_parameter('bias', None) + + self._alpha = None + + self.reset_parameters() + self.edge_weight = None + + def reset_parameters(self): + self.lin_src.reset_parameters() + self.lin_dst.reset_parameters() + glorot(self.att_src) + glorot(self.att_dst) + zeros(self.bias) + + def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj, + size: Size = None, return_attention_weights=None, edge_weight=None): + # type: (Union[Tensor, OptPairTensor], Tensor, Size, NoneType) -> Tensor # noqa + # type: (Union[Tensor, OptPairTensor], SparseTensor, Size, NoneType) -> Tensor # noqa + # type: (Union[Tensor, OptPairTensor], Tensor, Size, bool) -> Tuple[Tensor, Tuple[Tensor, Tensor]] # noqa + # type: (Union[Tensor, OptPairTensor], SparseTensor, Size, bool) -> Tuple[Tensor, SparseTensor] # noqa + r""" + Args: + return_attention_weights (bool, optional): If set to :obj:`True`, + will additionally return the tuple + :obj:`(edge_index, attention_weights)`, holding the computed + attention weights for each edge. (default: :obj:`None`) + """ + H, C = self.heads, self.out_channels + + # We first transform the input node features. If a tuple is passed, we + # transform source and target node features via separate weights: + if isinstance(x, Tensor): + assert x.dim() == 2, "Static graphs not supported in 'GATConv'" + x_src = x_dst = self.lin_src(x).view(-1, H, C) + else: # Tuple of source and target node features: + x_src, x_dst = x + assert x_src.dim() == 2, "Static graphs not supported in 'GATConv'" + x_src = self.lin_src(x_src).view(-1, H, C) + if x_dst is not None: + x_dst = self.lin_dst(x_dst).view(-1, H, C) + + x = (x_src, x_dst) + + # Next, we compute node-level attention coefficients, both for source + # and target nodes (if present): + alpha_src = (x_src * self.att_src).sum(dim=-1) + alpha_dst = None if x_dst is None else (x_dst * self.att_dst).sum(-1) + alpha = (alpha_src, alpha_dst) + + if self.add_self_loops: + if isinstance(edge_index, Tensor): + # We only want to add self-loops for nodes that appear both as + # source and target nodes: + num_nodes = x_src.size(0) + if x_dst is not None: + num_nodes = min(num_nodes, x_dst.size(0)) + num_nodes = min(size) if size is not None else num_nodes + # edge_index, _ = remove_self_loops(edge_index) + # edge_index, _ = add_self_loops(edge_index, num_nodes=num_nodes) + edge_index, edge_weight = remove_self_loops(edge_index, edge_weight) + edge_index, edge_weight = add_self_loops(edge_index, edge_weight, num_nodes=num_nodes) + self.edge_weight = edge_weight + # if edge_index.size(1) != self.edge_weight.shape[0]: + # self.edge_weight = None + + elif isinstance(edge_index, SparseTensor): + edge_index = set_diag(edge_index) + + # propagate_type: (x: OptPairTensor, alpha: OptPairTensor) + out = self.propagate(edge_index, x=x, alpha=alpha, size=size) + + alpha = self._alpha + assert alpha is not None + self._alpha = None + + if self.concat: + out = out.view(-1, self.heads * self.out_channels) + else: + out = out.mean(dim=1) + + if self.bias is not None: + out += self.bias + + if isinstance(return_attention_weights, bool): + if isinstance(edge_index, Tensor): + return out, (edge_index, alpha) + elif isinstance(edge_index, SparseTensor): + return out, edge_index.set_value(alpha, layout='coo') + else: + return out + + def message(self, x_j: Tensor, alpha_j: Tensor, alpha_i: OptTensor, + index: Tensor, ptr: OptTensor, + size_i: Optional[int]) -> Tensor: + # Given egel-level attention coefficients for source and target nodes, + # we simply need to sum them up to "emulate" concatenation: + alpha = alpha_j if alpha_i is None else alpha_j + alpha_i + + alpha = F.leaky_relu(alpha, self.negative_slope) + alpha = softmax(alpha, index, ptr, size_i) + self._alpha = alpha # Save for later use. + alpha = F.dropout(alpha, p=self.dropout, training=self.training) + + if self.edge_weight is not None: + x_j = self.edge_weight.view(-1, 1, 1) * x_j + return x_j * alpha.unsqueeze(-1) + + def __repr__(self): + return '{}({}, {}, heads={})'.format(self.__class__.__name__, + self.in_channels, + self.out_channels, self.heads) + + +class GAT(nn.Module): + def __init__(self, args): + super(GAT, self).__init__() + self.args = args + self.num_features = args.num_features + self.nhid = args.nhid + self.num_classes = args.num_classes + self.dropout_ratio = args.dropout_ratio + self.num_layers = args.num_layers + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + + self.convs.append(GATConv(self.num_features, self.nhid, heads=1, concat=False)) + self.bns.append(nn.BatchNorm1d(self.nhid)) + + for _ in range(self.num_layers - 1): + self.convs.append(GATConv(self.nhid, self.nhid, heads=1, concat=False)) + self.bns.append(nn.BatchNorm1d(self.nhid)) + + self.cls = torch.nn.Linear(self.nhid, self.num_classes) + + self.activation = F.relu + self.use_bn = args.use_bn + + def reset_parameters(self): + for conv in self.convs: + conv.reset_parameters() + for bn in self.bns: + bn.reset_parameters() + + def forward(self, x, edge_index, edge_weight=None): + x = self.feat_bottleneck(x, edge_index, edge_weight) + x = self.feat_classifier(x) + + return x + + def _ensure_contiguousness(self, x, edge_idx, edge_weight): + if not x.is_sparse: + x = x.contiguous() + if hasattr(edge_idx, 'contiguous'): + edge_idx = edge_idx.contiguous() + if edge_weight is not None: + edge_weight = edge_weight.contiguous() + + return x, edge_idx, edge_weight + + def feat_bottleneck(self, x, edge_index, edge_weight=None): + x, edge_index, edge_weight = self._ensure_contiguousness(x, edge_index, edge_weight) + + for i, conv in enumerate(self.convs): + x = conv(x, edge_index, edge_weight=edge_weight) + if self.use_bn: + x = self.bns[i](x) + x = self.activation(x) + x = F.dropout(x, p=self.dropout_ratio, training=self.training) + return x + + def feat_classifier(self, x): + x = self.cls(x) + + return x + + +class GINConv(MessagePassing): + def __init__(self, nn: Callable, eps: float = 0., train_eps: bool = False, + **kwargs): + kwargs.setdefault('aggr', 'add') + super().__init__(**kwargs) + self.nn = nn + self.initial_eps = eps + if train_eps: + self.eps = torch.nn.Parameter(torch.Tensor([eps])) + else: + self.register_buffer('eps', torch.Tensor([eps])) + + self.reset_parameters() + + def reset_parameters(self): + super().reset_parameters() + reset(self.nn) + self.eps.data.fill_(self.initial_eps) + + def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj, + size: Size = None) -> Tensor: + + if isinstance(x, Tensor): + x: OptPairTensor = (x, x) + + # propagate_type: (x: OptPairTensor) + out = self.propagate(edge_index, x=x, size=size) + + x_r = x[1] + if x_r is not None: + out = out + (1 + self.eps) * x_r + + return self.nn(out) + + def message(self, x_j: Tensor) -> Tensor: + return x_j + + def message_and_aggregate(self, adj_t: SparseTensor, + x: OptPairTensor) -> Tensor: + # if isinstance(adj_t, SparseTensor): + # adj_t = adj_t.set_value(None, layout=None) + return matmul(adj_t, x[0], reduce=self.aggr) + + def __repr__(self) -> str: + return f'{self.__class__.__name__}(nn={self.nn})' + + +class GIN(nn.Module): + def __init__(self, args): + super(GIN, self).__init__() + self.args = args + self.num_features = args.num_features + self.nhid = args.nhid + self.num_classes = args.num_classes + self.dropout_ratio = args.dropout_ratio + self.num_layers = args.num_layers + + self.convs = nn.ModuleList() + self.bns = nn.ModuleList() + + self.lin = torch.nn.Linear(self.num_features, self.nhid) + + self.convs.append(GINConv(Sequential(Linear(self.nhid, self.nhid)), train_eps=True)) + self.bns.append(nn.BatchNorm1d(self.nhid)) + + for _ in range(self.num_layers - 1): + self.convs.append(GINConv(Sequential(Linear(self.nhid, self.nhid)), train_eps=True)) + self.bns.append(nn.BatchNorm1d(self.nhid)) + + self.cls = torch.nn.Linear(self.nhid, self.num_classes) + + self.activation = F.relu + self.use_bn = args.use_bn + + def reset_parameters(self): + for conv in self.convs: + conv.reset_parameters() + for bn in self.bns: + bn.reset_parameters() + + def forward(self, x, edge_index, edge_weight=None): + x = self.feat_bottleneck(x, edge_index, edge_weight) + x = self.feat_classifier(x) + + return x + + def feat_bottleneck(self, x, edge_index, edge_weight=None): + x = self.lin(x) + + if edge_weight is not None: + adj = SparseTensor.from_edge_index(edge_index, edge_weight, sparse_sizes=2 * x.shape[:1]).t() + + for i, conv in enumerate(self.convs): + if edge_weight is not None: + x = conv(x, adj) + else: + x = conv(x, edge_index, edge_weight) + if self.use_bn: + x = self.bns[i](x) + x = self.activation(x) + x = F.dropout(x, p=self.dropout_ratio, training=self.training) + return x + + def feat_classifier(self, x): + x = self.cls(x) + + return x diff --git a/model.py b/model.py new file mode 100644 index 0000000..3514844 --- /dev/null +++ b/model.py @@ -0,0 +1,40 @@ +import torch +import torch.nn.functional as F +from layer import * + + +class Model(torch.nn.Module): + def __init__(self, args): + super(Model, self).__init__() + self.args = args + + if args.gnn == 'gcn': + self.gnn = GCN(args) + elif args.gnn == 'sage': + self.gnn = SAGE(args) + elif args.gnn == 'gat': + self.gnn = GAT(args) + elif args.gnn == 'gin': + self.gnn = GIN(args) + else: + assert args.gnn in ('gcn', 'sage', 'gat', 'gin'), 'Invalid gnn' + + self.reset_parameters() + + def reset_parameters(self): + self.gnn.reset_parameters() + + def forward(self, x, edge_index, edge_weight=None): + x = self.feat_bottleneck(x, edge_index, edge_weight) + x = self.feat_classifier(x) + + return F.log_softmax(x, dim=1) + + def feat_bottleneck(self, x, edge_index, edge_weight=None): + x = self.gnn.feat_bottleneck(x, edge_index, edge_weight) + return x + + def feat_classifier(self, x): + x = self.gnn.feat_classifier(x) + + return x diff --git a/train_source.py b/train_source.py new file mode 100644 index 0000000..aaac6ed --- /dev/null +++ b/train_source.py @@ -0,0 +1,133 @@ +import argparse +import glob +import os +import time + +import torch +import torch.nn.functional as F +from model import * +from utils import * +from datasets import * +import numpy as np + +parser = argparse.ArgumentParser() + +parser.add_argument('--seed', type=int, default=200, help='random seed') +parser.add_argument('--lr', type=float, default=0.0001, help='learning rate') +parser.add_argument('--weight_decay', type=float, default=0.001, help='weight decay') +parser.add_argument('--nhid', type=int, default=128, help='hidden size') +parser.add_argument('--dropout_ratio', type=float, default=0.1, help='dropout ratio') +parser.add_argument('--device', type=str, default='cuda:2', help='specify cuda devices') +parser.add_argument('--source', type=str, default='Citationv1', help='source domain data') +parser.add_argument('--target', type=str, default='DBLPv7', help='target domain data') +parser.add_argument('--epochs', type=int, default=1000, help='maximum number of epochs') +parser.add_argument('--patience', type=int, default=100, help='patience for early stopping') +parser.add_argument('--num_layers', type=int, default=2, help='number of gnn layers') +parser.add_argument('--gnn', type=str, default='gcn', help='different types of gnns') +parser.add_argument('--use_bn', type=bool, default=False, help='do not use batchnorm') + +args = parser.parse_args() + +if args.source in {'DBLPv7', 'ACMv9', 'Citationv1'}: + path = osp.join(osp.dirname(osp.realpath(__file__)), '.', 'data/Citation', args.source) + source_dataset = CitationDataset(path, args.source) +elif args.source in {'S10', 'M10', 'E10'}: + path = osp.join(osp.dirname(osp.realpath(__file__)), '.', 'data/Elliptic', args.source) + source_dataset = EllipticDataset(path, args.source) +elif args.source in {'DE', 'EN', 'FR'}: + path = osp.join(osp.dirname(osp.realpath(__file__)), '.', 'data/Twitch', args.source) + source_dataset = TwitchDataset(path, args.source) + +if args.target in {'DBLPv7', 'ACMv9', 'Citationv1'}: + path = osp.join(osp.dirname(osp.realpath(__file__)), '.', 'data/Citation', args.target) + target_dataset = CitationDataset(path, args.target) +elif args.target in {'S10', 'M10', 'E10'}: + path = osp.join(osp.dirname(osp.realpath(__file__)), '.', 'data/Elliptic', args.target) + target_dataset = EllipticDataset(path, args.target) +elif args.target in {'DE', 'EN', 'FR'}: + path = osp.join(osp.dirname(osp.realpath(__file__)), '.', 'data/Twitch', args.target) + target_dataset = TwitchDataset(path, args.target) + +target_data = target_dataset[0] +data = source_dataset[0] + +args.num_classes = len(np.unique(data.y.numpy())) +args.num_features = data.x.size(1) + +print(args) + +model = Model(args).to(args.device) +data = data.to(args.device) +optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) + +def train_source(): + min_loss = 1e10 + patience_cnt = 0 + val_loss_values = [] + best_epoch = 0 + + t = time.time() + for epoch in range(args.epochs): + model.train() + optimizer.zero_grad() + correct = 0 + output = model(data.x, data.edge_index) + train_loss = F.nll_loss(output[data.train_mask], data.y[data.train_mask]) + train_loss.backward() + optimizer.step() + pred = output[data.train_mask].max(dim=1)[1] + correct = pred.eq(data.y[data.train_mask]).sum().item() + train_acc = correct * 1.0 / (data.train_mask).sum().item() + + val_acc, val_loss = compute_test(data.val_mask, model, data) + + print('Epoch: {:04d}'.format(epoch + 1), 'train_loss: {:.6f}'.format(train_loss), + 'train_acc: {:.6f}'.format(train_acc), 'loss_val: {:.6f}'.format(val_loss), + 'acc_val: {:.6f}'.format(val_acc), 'time: {:.6f}s'.format(time.time() - t)) + + val_loss_values.append(val_loss) + torch.save(model.state_dict(), '{}.pth'.format(epoch)) + + if val_loss_values[-1] < min_loss: + min_loss = val_loss_values[-1] + best_epoch = epoch + patience_cnt = 0 + else: + patience_cnt += 1 + + if patience_cnt == args.patience: + break + + files = glob.glob('*.pth') + for f in files: + epoch_nb = int(f.split('.')[0]) + if epoch_nb < best_epoch: + os.remove(f) + + files = glob.glob('*.pth') + for f in files: + epoch_nb = int(f.split('.')[0]) + if epoch_nb > best_epoch: + os.remove(f) + print('Optimization Finished! Total time elapsed: {:.6f}'.format(time.time() - t)) + + return best_epoch + + +if __name__ == '__main__': + if os.path.exists('model.pth'): + os.remove('model.pth') + # Model training + best_model = train_source() + # Restore best model for test set + model.load_state_dict(torch.load('{}.pth'.format(best_model))) + test_acc, test_loss = compute_test(data.test_mask, model, data) + print('Source {} test set results, loss = {:.6f}, accuracy = {:.6f}'.format(args.source, test_loss, test_acc)) + + target_data = target_data.to(args.device) + test_acc, test_loss = evaluate(target_data.x, target_data.edge_index, target_data.edge_weight, target_data.y, model) + print('Target {} test results, loss = {:.6f}, accuracy = {:.6f}'.format(args.target, test_loss, test_acc)) + + # Save model for target domain adaptation + torch.save(model.state_dict(), 'model.pth') + os.remove('{}.pth'.format(best_model)) diff --git a/train_target.py b/train_target.py new file mode 100644 index 0000000..6111ede --- /dev/null +++ b/train_target.py @@ -0,0 +1,312 @@ +import argparse +import glob +import os +import time + +import torch +import torch.nn.functional as F +from model import * +from utils import * +from layer import * +from datasets import * +import numpy as np +from torch_geometric.transforms import Constant +from torch.nn.parameter import Parameter +from torch_geometric.utils import dropout_adj +from tqdm import tqdm +import random + +from torch import Tensor + +parser = argparse.ArgumentParser() + +parser.add_argument('--seed', type=int, default=200, help='random seed') +parser.add_argument('--lr', type=float, default=0.0001, help='learning rate') +parser.add_argument('--weight_decay', type=float, default=0.001, help='weight decay') +parser.add_argument('--nhid', type=int, default=128, help='hidden size') +parser.add_argument('--dropout_ratio', type=float, default=0.1, help='dropout ratio') +parser.add_argument('--device', type=str, default='cuda:2', help='specify cuda devices') +parser.add_argument('--source', type=str, default='Citationv1', help='source domain data') +parser.add_argument('--target', type=str, default='DBLPv7', help='target domain data') +parser.add_argument('--epochs', type=int, default=1000, help='maximum number of epochs') +parser.add_argument('--momentum', type=float, default=0.9, help='momentum') +parser.add_argument('--tau', type=float, default=0.2, help='tau') +parser.add_argument('--lamb', type=float, default=0.2, help='trade-off parameter lambda') +parser.add_argument('--num_layers', type=int, default=2, help='number of gnn layers') +parser.add_argument('--gnn', type=str, default='gcn', help='different types of gnns') +parser.add_argument('--use_bn', type=bool, default=False, help='do not use batchnorm') +parser.add_argument('--make_undirected', type=bool, default=True, help='directed graph or not') + +parser.add_argument('--ratio', type=float, default=0.2, help='structure perturbation budget') +parser.add_argument('--loop_adj', type=int, default=1, help='inner loop for adjacent update') +parser.add_argument('--loop_feat', type=int, default=2, help='inner loop for feature update') +parser.add_argument('--loop_model', type=int, default=3, help='inner loop for model update') +parser.add_argument('--debug', type=int, default=1, help='whether output intermediate results') +parser.add_argument("--K", type=int, default=5, help='number of k-nearest neighbors') + +args = parser.parse_args() + + +if args.target in {'DBLPv7', 'ACMv9', 'Citationv1'}: + path = osp.join(osp.dirname(osp.realpath(__file__)), '.', 'data/Citation', args.target) + target_dataset = CitationDataset(path, args.target) +elif args.target in {'S10', 'M10', 'E10'}: + path = osp.join(osp.dirname(osp.realpath(__file__)), '.', 'data/Elliptic', args.target) + target_dataset = EllipticDataset(path, args.target) +elif args.target in {'DE', 'EN', 'FR'}: + path = osp.join(osp.dirname(osp.realpath(__file__)), '.', 'data/Twitch', args.target) + target_dataset = TwitchDataset(path, args.target) + +data = target_dataset[0] + +args.num_classes = len(np.unique(data.y.numpy())) +args.num_features = data.x.size(1) + +print(args) + +model = Model(args).to(args.device) +data = data.to(args.device) + +neighprop = NeighborPropagate() + +model.load_state_dict(torch.load('model.pth')) + +delta_feat = Parameter(torch.FloatTensor(data.x.size(0), data.x.size(1)).to(args.device)) +delta_feat.data.fill_(1e-7) +optimizer_feat = torch.optim.Adam([delta_feat], lr=0.0001, weight_decay=0.0001) + +modified_edge_index = data.edge_index.clone() +modified_edge_index = modified_edge_index[:, modified_edge_index[0] < modified_edge_index[1]] +row, col = modified_edge_index[0], modified_edge_index[1] +edge_index_id = (2 * data.x.size(0) - row - 1) * row // 2 + col - row - 1 +edge_index_id = edge_index_id.long() +modified_edge_index = linear_to_triu_idx(data.x.size(0), edge_index_id) +perturbed_edge_weight = torch.full_like(edge_index_id, 1e-7, dtype=torch.float32, requires_grad=True).to(args.device) + +optimizer_adj = torch.optim.Adam([perturbed_edge_weight], lr=0.0001, weight_decay=0.0001) + +n_perturbations = int(args.ratio * data.edge_index.shape[1] // 2) + +n = data.x.size(0) + +def train_target(target_data, perturbed_edge_weight): + optimizer_model = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) + t = time.time() + edge_index = target_data.edge_index + edge_weight = torch.ones(edge_index.shape[1]).to(args.device) + feat = target_data.x + + mem_fea = torch.rand(target_data.x.size(0), args.nhid).to(args.device) + mem_cls = torch.ones(target_data.x.size(0), args.num_classes).to(args.device) / args.num_classes + + for it in tqdm(range(args.epochs//(args.loop_feat+args.loop_adj))): + for loop_model in range(args.loop_model): + for k,v in model.named_parameters(): + v.requires_grad = True + model.train() + feat = feat.detach() + edge_weight = edge_weight.detach() + + optimizer_model.zero_grad() + feat_output = model.feat_bottleneck(feat, edge_index, edge_weight) + cls_output = model.feat_classifier(feat_output) + + onehot = torch.nn.functional.one_hot(cls_output.argmax(1), num_classes=args.num_classes).float() + proto = (torch.mm(mem_fea.t(), onehot) / (onehot.sum(dim=0) + 1e-8)).t() + + prob = neighprop(mem_cls, edge_index) + weight, pred = torch.max(prob, dim=1) + cl, weight_ = instance_proto_alignment(feat_output, proto, pred) + ce = F.cross_entropy(cls_output, pred, reduction='none') + loss_local = torch.sum(weight_ * ce) / (torch.sum(weight_).item()) + loss = loss_local * (1 - args.lamb) + cl * args.lamb + + loss.backward() + optimizer_model.step() + print('Model: ' + str(loss.item())) + + model.eval() + with torch.no_grad(): + feat_output = model.feat_bottleneck(feat, edge_index, edge_weight) + cls_output = model.feat_classifier(feat_output) + softmax_out = F.softmax(cls_output, dim=1) + outputs_target = softmax_out**2 / ((softmax_out**2).sum(dim=0)) + + mem_cls = (1.0 - args.momentum) * mem_cls + args.momentum * outputs_target.clone() + mem_fea = (1.0 - args.momentum) * mem_fea + args.momentum * feat_output.clone() + + for k,v in model.named_parameters(): + v.requires_grad = False + + perturbed_edge_weight = perturbed_edge_weight.detach() + for loop_feat in range(args.loop_feat): + optimizer_feat.zero_grad() + delta_feat.requires_grad = True + loss = test_time_loss(model, target_data.x + delta_feat, edge_index, mem_fea, mem_cls, edge_weight) + loss.backward() + optimizer_feat.step() + print('Feat: ' + str(loss.item())) + + new_feat = (data.x + delta_feat).detach() + for loop_adj in range(args.loop_adj): + perturbed_edge_weight.requires_grad = True + edge_index, edge_weight = get_modified_adj(modified_edge_index, perturbed_edge_weight, n, args.device, edge_index, edge_weight, args.make_undirected) + loss = test_time_loss(model, new_feat, edge_index, mem_fea, mem_cls, edge_weight) + print('Adj: ' + str(loss.item())) + + gradient = grad_with_checkpoint(loss, perturbed_edge_weight)[0] + + with torch.no_grad(): + update_edge_weights(gradient) + perturbed_edge_weight = project(n_perturbations, perturbed_edge_weight, 1e-7) + + if args.loop_adj != 0: + edge_index, edge_weight = get_modified_adj(modified_edge_index, perturbed_edge_weight, n, args.device, edge_index, edge_weight, args.make_undirected) + edge_weight = edge_weight.detach() + + if args.loop_feat != 0: + feat = (target_data.x + delta_feat).detach() + + edge_index, edge_weight = sample_final_edges(n_perturbations, perturbed_edge_weight, target_data, modified_edge_index, mem_fea, mem_cls) + + test_acc, _ = evaluate(target_data.x + delta_feat, edge_index, edge_weight, target_data.y, model) + print('acc : ' + str(test_acc)) + print('Optimization Finished!\n') + + +def instance_proto_alignment(feat, center, pred): + feat_norm = F.normalize(feat, dim=1) + center_norm = F.normalize(center, dim=1) + sim = torch.matmul(feat_norm, center_norm.t()) + + num_nodes = feat.size(0) + weight = sim[range(num_nodes), pred] + sim = torch.exp(sim / args.tau) + pos_sim = sim[range(num_nodes), pred] + + sim_feat = torch.matmul(feat_norm, feat_norm.t()) + sim_feat = torch.exp(sim_feat / args.tau) + ident = sim_feat[range(num_nodes), range(num_nodes)] + + logit = pos_sim / (sim.sum(dim=1) - pos_sim + sim_feat.sum(dim=1) - ident + 1e-8) + loss = - torch.log(logit + 1e-8).mean() + + return loss, weight + + +def update_edge_weights(gradient): + optimizer_adj.zero_grad() + perturbed_edge_weight.grad = gradient + optimizer_adj.step() + perturbed_edge_weight.data[perturbed_edge_weight < 1e-7] = 1e-7 + + +def test_time_loss(model, feat, edge_index, mem_fea, mem_cls, edge_weight=None): + model.eval() + feat_output = model.feat_bottleneck(feat, edge_index, edge_weight) + cls_output = model.feat_classifier(feat_output) + softmax_out = F.softmax(cls_output, dim=1) + _, predict = torch.max(softmax_out, 1) + mean_ent = Entropy(softmax_out) + est_p = (mean_ent n_perturbations: + n_samples = sampled_edges.sum() + if args.debug ==2: + print(f'{i}-th sampling: too many samples {n_samples}') + continue + + perturbed_edge_weight = sampled_edges + + edge_index, edge_weight = get_modified_adj(modified_edge_index, perturbed_edge_weight, n, args.device, edge_index, edge_weight, args.make_undirected) + with torch.no_grad(): + loss = test_time_loss(model, feat, edge_index, mem_fea, mem_cls, edge_weight) + + # Save best sample + if best_loss > loss: + best_loss = loss + print('best_loss:', best_loss.item()) + best_edges = perturbed_edge_weight.clone().cpu() + + # Recover best sample + perturbed_edge_weight.data.copy_(best_edges.to(args.device)) + + edge_index, edge_weight = get_modified_adj(modified_edge_index, perturbed_edge_weight, n, args.device, edge_index, edge_weight, args.make_undirected) + edge_mask = edge_weight == 1 + make_undirected = args.make_undirected + + allowed_perturbations = 2 * n_perturbations if make_undirected else n_perturbations + edges_after_attack = edge_mask.sum() + clean_edges = edge_index.shape[1] + assert (edges_after_attack >= clean_edges - allowed_perturbations + and edges_after_attack <= clean_edges + allowed_perturbations), \ + f'{edges_after_attack} out of range with {clean_edges} clean edges and {n_perturbations} pertutbations' + + return edge_index[:, edge_mask], edge_weight[edge_mask] + +if __name__ == '__main__': + train_target(data, perturbed_edge_weight) diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..f6f2add --- /dev/null +++ b/utils.py @@ -0,0 +1,132 @@ +import os.path as osp +import torch +import torch.nn.functional as F +from torch import Tensor +from torch_sparse import coalesce + +import warnings +warnings.filterwarnings('ignore', category=DeprecationWarning) + + +def grad_with_checkpoint(outputs, inputs): + inputs = (inputs,) if isinstance(inputs, torch.Tensor) else tuple(inputs) + for input in inputs: + if not input.is_leaf: + input.retain_grad() + torch.autograd.backward(outputs) + + grad_outputs = [] + for input in inputs: + grad_outputs.append(input.grad.clone()) + input.grad.zero_() + return grad_outputs + + +def linear_to_triu_idx(n: int, lin_idx: torch.Tensor) -> torch.Tensor: + row_idx = ( + n + - 2 + - torch.floor(torch.sqrt(-8 * lin_idx.double() + 4 * n * (n - 1) - 7) / 2.0 - 0.5) + ).long() + col_idx = ( + lin_idx + + row_idx + + 1 - n * (n - 1) // 2 + + (n - row_idx) * ((n - row_idx) - 1) // 2 + ) + return torch.stack((row_idx, col_idx)) + + +def bisection(edge_weights, a, b, n_perturbations, epsilon=1e-5, iter_max=1e5): + def func(x): + return torch.clamp(edge_weights - x, 0, 1).sum() - n_perturbations + + miu = a + for i in range(int(iter_max)): + miu = (a + b) / 2 + # Check if middle point is root + if (func(miu) == 0.0): + break + # Decide the side to repeat the steps + if (func(miu) * func(a) < 0): + b = miu + else: + a = miu + if ((b - a) <= epsilon): + break + return miu + + +def project(n_perturbations, values, eps, inplace=False): + if not inplace: + values = values.clone() + + if torch.clamp(values, 0, 1).sum() > n_perturbations: + left = (values - 1).min() + right = values.max() + miu = bisection(values, left, right, n_perturbations) + values.data.copy_(torch.clamp( + values - miu, min=eps, max=1 - eps + )) + else: + values.data.copy_(torch.clamp(values, min=eps, max=1 - eps)) + + return values + + +def get_modified_adj(modified_edge_index, perturbed_edge_weight, n, device, edge_index, edge_weight, make_undirected=False): + if make_undirected: + modified_edge_index, modified_edge_weight = to_symmetric(modified_edge_index, perturbed_edge_weight, n) + else: + modified_edge_index, modified_edge_weight = modified_edge_index, perturbed_edge_weight + edge_index = torch.cat((edge_index.to(device), modified_edge_index), dim=-1) + edge_weight = torch.cat((edge_weight.to(device), modified_edge_weight)) + edge_index, edge_weight = coalesce(edge_index, edge_weight, m=n, n=n, op='sum') + + # Allow removal of edges + edge_weight[edge_weight > 1] = 2 - edge_weight[edge_weight > 1] + return edge_index, edge_weight + + +def to_symmetric(edge_index, edge_weight, n, op='mean'): + symmetric_edge_index = torch.cat( + (edge_index, edge_index.flip(0)), dim=-1 + ) + + symmetric_edge_weight = edge_weight.repeat(2) + + symmetric_edge_index, symmetric_edge_weight = coalesce( + symmetric_edge_index, + symmetric_edge_weight, + m=n, + n=n, + op=op + ) + return symmetric_edge_index, symmetric_edge_weight + + +def compute_test(mask, model, data): + model.eval() + output = model(data.x, data.edge_index) + loss = F.nll_loss(output[mask], data.y[mask]) + pred = output[mask].max(dim=1)[1] + correct = pred.eq(data.y[mask]).sum().item() + acc = correct * 1.0 / (mask.sum().item()) + + return acc, loss + + +def evaluate(x, edge_index, edge_weight, y, model): + model.eval() + output = model(x, edge_index, edge_weight) + loss = F.nll_loss(output, y) + pred = output.max(dim=1)[1] + correct = pred.eq(y).sum().item() + acc = correct * 1.0 / len(y) + + return acc, loss + +def Entropy(input_): + entropy = -input_ * torch.log(input_ + 1e-8) + entropy = torch.sum(entropy, dim=1) + return entropy

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