{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T05:19:05Z","timestamp":1669267145579},"publisher-location":"New York, NY, USA","reference-count":60,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T00:00:00Z","timestamp":1676419200000},"content-version":"vor","delay-in-days":369,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1652492"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,2,11]]},"DOI":"10.1145\/3488560.3498492","type":"proceedings-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T21:42:57Z","timestamp":1644961377000},"source":"Crossref","is-referenced-by-count":0,"title":["Attributed Graph Modeling with Vertex Replacement Grammars"],"prefix":"10.1145","author":[{"given":"Satyaki","family":"Sikdar","sequence":"first","affiliation":[{"name":"University of Notre Dame, Notre Dame, IN, USA"}]},{"given":"Neil","family":"Shah","sequence":"additional","affiliation":[{"name":"Snap Inc., Santa Monica, CA, USA"}]},{"given":"Tim","family":"Weninger","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, IN, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Learning Hyperedge Replacement Grammars for Graph Generation. TPAMI","author":"Salvador Agui","year":"2018","unstructured":"Salvador Agui naga, David Chiang , and Tim Weninger . 2018. Learning Hyperedge Replacement Grammars for Graph Generation. TPAMI ( 2018 ). Salvador Agui naga, David Chiang, and Tim Weninger. 2018. Learning Hyperedge Replacement Grammars for Graph Generation. TPAMI (2018)."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Salvador Agui naga Rodrigo Palacios David Chiang and Tim Weninger. 2016. Growing Graphs from Hyperedge Replacement Graph Grammars. In CIKM. ACM. Salvador Agui naga Rodrigo Palacios David Chiang and Tim Weninger. 2016. Growing Graphs from Hyperedge Replacement Graph Grammars. In CIKM. ACM.","DOI":"10.1145\/2983323.2983826"},{"key":"e_1_3_2_2_3_1","volume-title":"Efficient graphlet counting for large networks","author":"Ahmed Nesreen K","unstructured":"Nesreen K Ahmed , Jennifer Neville , Ryan A Rossi , and Nick Duffield . 2015. Efficient graphlet counting for large networks . In ICDM. IEEE. Nesreen K Ahmed, Jennifer Neville, Ryan A Rossi, and Nick Duffield. 2015. Efficient graphlet counting for large networks. In ICDM. IEEE."},{"key":"e_1_3_2_2_4_1","volume-title":"Oddball: Spotting anomalies in weighted graphs","author":"Akoglu Leman","year":"2010","unstructured":"Leman Akoglu , Mary McGlohon , and Christos Faloutsos . 2010 . Oddball: Spotting anomalies in weighted graphs . In PAKDD. Springer . Leman Akoglu, Mary McGlohon, and Christos Faloutsos. 2010. Oddball: Spotting anomalies in weighted graphs. In PAKDD. Springer."},{"key":"e_1_3_2_2_5_1","volume-title":"Ddgk: Learning graph representations for deep divergence graph kernels. In WWW . 37--48.","author":"Al-Rfou Rami","year":"2019","unstructured":"Rami Al-Rfou , Bryan Perozzi , and Dustin Zelle . 2019 . Ddgk: Learning graph representations for deep divergence graph kernels. In WWW . 37--48. Rami Al-Rfou, Bryan Perozzi, and Dustin Zelle. 2019. Ddgk: Learning graph representations for deep divergence graph kernels. In WWW . 37--48."},{"key":"e_1_3_2_2_6_1","volume-title":"Fast unfolding of communities in large networks. JSM","author":"Blondel Vincent D","year":"2008","unstructured":"Vincent D Blondel , Jean-Loup Guillaume , Renaud Lambiotte , and Etienne Lefebvre . 2008. Fast unfolding of communities in large networks. JSM ( 2008 ). Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008. Fast unfolding of communities in large networks. JSM (2008)."},{"key":"e_1_3_2_2_7_1","unstructured":"Aleksandar Bojchevski Oleksandr Shchur Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2018. NetGAN: Generating Graphs via Random Walks. In ICML . Aleksandar Bojchevski Oleksandr Shchur Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2018. NetGAN: Generating Graphs via Random Walks. In ICML ."},{"key":"e_1_3_2_2_8_1","volume-title":"From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering. arXiv preprint arXiv:2010.00402","author":"Chami Ines","year":"2020","unstructured":"Ines Chami , Albert Gu , Vaggos Chatziafratis , and Christopher R\u00e9. 2020. From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering. arXiv preprint arXiv:2010.00402 ( 2020 ). Ines Chami, Albert Gu, Vaggos Chatziafratis, and Christopher R\u00e9. 2020. From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering. arXiv preprint arXiv:2010.00402 (2020)."},{"key":"e_1_3_2_2_9_1","volume-title":"Jon Michael Kleinberg, and Jure Leskovec","author":"Cheng Justin","year":"2014","unstructured":"Justin Cheng , Lada Adamic , P Alex Dow , Jon Michael Kleinberg, and Jure Leskovec . 2014 . Can cascades be predicted?. In WWW . Justin Cheng, Lada Adamic, P Alex Dow, Jon Michael Kleinberg, and Jure Leskovec. 2014. Can cascades be predicted?. In WWW ."},{"key":"e_1_3_2_2_10_1","volume-title":"The average distances in random graphs with given expected degrees. PNAS","author":"Chung Fan","year":"2002","unstructured":"Fan Chung and Linyuan Lu. 2002. The average distances in random graphs with given expected degrees. PNAS ( 2002 ). Fan Chung and Linyuan Lu. 2002. The average distances in random graphs with given expected degrees. PNAS (2002)."},{"key":"e_1_3_2_2_11_1","volume-title":"Carlo Sansone, and Mario Vento","author":"Cordella Luigi P","year":"2004","unstructured":"Luigi P Cordella , Pasquale Foggia , Carlo Sansone, and Mario Vento . 2004 . A (sub) graph isomorphism algorithm for matching large graphs. PAMI ( 2004). Luigi P Cordella, Pasquale Foggia, Carlo Sansone, and Mario Vento. 2004. A (sub) graph isomorphism algorithm for matching large graphs. PAMI (2004)."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Sanjoy Dasgupta. 2016. A cost function for similarity-based hierarchical clustering. In STOC . Sanjoy Dasgupta. 2016. A cost function for similarity-based hierarchical clustering. In STOC .","DOI":"10.1145\/2897518.2897527"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"Yuxiao Dong Nitesh V Chawla and Ananthram Swami. 2017a. metapath2vec: Scalable representation learning for heterogeneous networks. In KDD . 135--144. Yuxiao Dong Nitesh V Chawla and Ananthram Swami. 2017a. metapath2vec: Scalable representation learning for heterogeneous networks. In KDD . 135--144.","DOI":"10.1145\/3097983.3098036"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Yuxiao Dong Reid A Johnson Jian Xu and Nitesh V Chawla. 2017b. Structural diversity and homophily: A study across more than one hundred big networks. In KDD . 807--816. Yuxiao Dong Reid A Johnson Jian Xu and Nitesh V Chawla. 2017b. Structural diversity and homophily: A study across more than one hundred big networks. In KDD . 807--816.","DOI":"10.1145\/3097983.3098116"},{"key":"e_1_3_2_2_15_1","unstructured":"Hongchang Gao and Heng Huang. 2018. Deep attributed network embedding. In IJCAI . Hongchang Gao and Heng Huang. 2018. Deep attributed network embedding. In IJCAI ."},{"key":"e_1_3_2_2_16_1","volume-title":"Fast algorithms for frequent itemset mining using fp-trees. TKDE","author":"Grahne G\u00f6sta","year":"2005","unstructured":"G\u00f6sta Grahne and Jianfei Zhu . 2005. Fast algorithms for frequent itemset mining using fp-trees. TKDE ( 2005 ). G\u00f6sta Grahne and Jianfei Zhu. 2005. Fast algorithms for frequent itemset mining using fp-trees. TKDE (2005)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/43.159993"},{"key":"e_1_3_2_2_18_1","volume-title":"BigData","author":"Hibshman Justus","unstructured":"Justus Hibshman , Satyaki Sikdar , and Tim Weninger . 2019. Towards interpretable graph modeling with vertex replacement grammars . In BigData . IEEE. Justus Hibshman, Satyaki Sikdar, and Tim Weninger. 2019. Towards interpretable graph modeling with vertex replacement grammars. In BigData. IEEE."},{"key":"e_1_3_2_2_19_1","volume-title":"Alex Beutel, Neil Shah, Kijung Shin, and Christos Faloutsos.","author":"Hooi Bryan","year":"2016","unstructured":"Bryan Hooi , Hyun Ah Song , Alex Beutel, Neil Shah, Kijung Shin, and Christos Faloutsos. 2016 . Fraudar : Bounding graph fraud in the face of camouflage. In KDD . Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, and Christos Faloutsos. 2016. Fraudar: Bounding graph fraud in the face of camouflage. In KDD ."},{"key":"e_1_3_2_2_20_1","volume-title":"Stochastic blockmodels and community structure in networks. Phys. Rev. E","author":"Karrer Brian","year":"2011","unstructured":"Brian Karrer and Mark EJ Newman . 2011. Stochastic blockmodels and community structure in networks. Phys. Rev. E ( 2011 ). Brian Karrer and Mark EJ Newman. 2011. Stochastic blockmodels and community structure in networks. Phys. Rev. E (2011)."},{"key":"e_1_3_2_2_21_1","volume-title":"Semi-supervised classification with graph convolutional networks. ICLR","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016a. Semi-supervised classification with graph convolutional networks. ICLR ( 2016 ). Thomas N Kipf and Max Welling. 2016a. Semi-supervised classification with graph convolutional networks. ICLR (2016)."},{"key":"e_1_3_2_2_22_1","volume-title":"Variational graph auto-encoders. arXiv preprint arXiv:1611.07308","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016b. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 ( 2016 ). Thomas N Kipf and Max Welling. 2016b. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)."},{"key":"e_1_3_2_2_23_1","volume-title":"Kronecker graphs: An approach to modeling networks. JMLR","author":"Leskovec Jure","year":"2010","unstructured":"Jure Leskovec , Deepayan Chakrabarti , Jon Kleinberg , Christos Faloutsos , and Zoubin Ghahramani . 2010. Kronecker graphs: An approach to modeling networks. JMLR ( 2010 ). Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos, and Zoubin Ghahramani. 2010. Kronecker graphs: An approach to modeling networks. JMLR (2010)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Miller McPherson Lynn Smith-Lovin and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annu. Rev. Sociol. (2001). Miller McPherson Lynn Smith-Lovin and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annu. Rev. Sociol. (2001).","DOI":"10.1146\/annurev.soc.27.1.415"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2659480.2659495"},{"key":"e_1_3_2_2_26_1","volume-title":"Mixing patterns in networks. Physical review E","author":"Newman Mark EJ","year":"2003","unstructured":"Mark EJ Newman . 2003. Mixing patterns in networks. Physical review E ( 2003 ). Mark EJ Newman. 2003. Mixing patterns in networks. Physical review E (2003)."},{"key":"e_1_3_2_2_27_1","unstructured":"Andrew Y Ng Michael I Jordan and Yair Weiss. 2002. On spectral clustering: Analysis and an algorithm. In NeurIPS. 849--856. Andrew Y Ng Michael I Jordan and Yair Weiss. 2002. On spectral clustering: Analysis and an algorithm. In NeurIPS. 849--856."},{"key":"e_1_3_2_2_28_1","volume-title":"DSAA","author":"Nilforoshan Hamed","unstructured":"Hamed Nilforoshan and Neil Shah . 2019. Slicendice: mining suspicious multi-attribute entity groups with multi-view graphs . In DSAA . IEEE. Hamed Nilforoshan and Neil Shah. 2019. Slicendice: mining suspicious multi-attribute entity groups with multi-view graphs. In DSAA . IEEE."},{"key":"e_1_3_2_2_29_1","volume-title":"Patricia Iglesias S\u00e1nchez, and Emmanuel M\u00fcller","author":"Perozzi Bryan","year":"2014","unstructured":"Bryan Perozzi , Leman Akoglu , Patricia Iglesias S\u00e1nchez, and Emmanuel M\u00fcller . 2014 a. Focused clustering and outlier detection in large attributed graphs. In KDD . Bryan Perozzi, Leman Akoglu, Patricia Iglesias S\u00e1nchez, and Emmanuel M\u00fcller. 2014a. Focused clustering and outlier detection in large attributed graphs. In KDD ."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/SocialCom-PASSAT.2012.130"},{"key":"e_1_3_2_2_32_1","volume-title":"Jennifer Neville, and Brian Gallagher.","author":"Joseph J","year":"2014","unstructured":"Joseph J Pfeiffer III, Sebastian Moreno , Timothy La Fond , Jennifer Neville, and Brian Gallagher. 2014 . Attributed graph models: Modeling network structure with correlated attributes. In WWW . 831--842. Joseph J Pfeiffer III, Sebastian Moreno, Timothy La Fond, Jennifer Neville, and Brian Gallagher. 2014. Attributed graph models: Modeling network structure with correlated attributes. In WWW . 831--842."},{"key":"e_1_3_2_2_33_1","volume-title":"Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E","author":"Raghavan Usha Nandini","year":"2007","unstructured":"Usha Nandini Raghavan , R\u00e9ka Albert , and Soundar Kumara . 2007. Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E ( 2007 ). Usha Nandini Raghavan, R\u00e9ka Albert, and Soundar Kumara. 2007. Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E (2007)."},{"key":"e_1_3_2_2_34_1","volume-title":"Hierarchical organization in complex networks. Phys. Rev. E","author":"Ravasz Erzs\u00e9bet","year":"2003","unstructured":"Erzs\u00e9bet Ravasz and Albert-L\u00e1szl\u00f3 Barab\u00e1si . 2003. Hierarchical organization in complex networks. Phys. Rev. E ( 2003 ). Erzs\u00e9bet Ravasz and Albert-L\u00e1szl\u00f3 Barab\u00e1si. 2003. Hierarchical organization in complex networks. Phys. Rev. E (2003)."},{"key":"e_1_3_2_2_35_1","volume-title":"International Conference on Machine Learning. PMLR.","author":"Rendsburg Luca","year":"2020","unstructured":"Luca Rendsburg , Holger Heidrich , and Ulrike Von Luxburg . 2020 . NetGAN without GAN: From Random Walks to Low-Rank Approximations . In International Conference on Machine Learning. PMLR. Luca Rendsburg, Holger Heidrich, and Ulrike Von Luxburg. 2020. NetGAN without GAN: From Random Walks to Low-Rank Approximations. In International Conference on Machine Learning. PMLR."},{"key":"e_1_3_2_2_36_1","volume-title":"An introduction to exponential random graph (p*) models for social networks. Social networks","author":"Robins Garry","year":"2007","unstructured":"Garry Robins , Pip Pattison , Yuval Kalish , and Dean Lusher . 2007. An introduction to exponential random graph (p*) models for social networks. Social networks ( 2007 ). Garry Robins, Pip Pattison, Yuval Kalish, and Dean Lusher. 2007. An introduction to exponential random graph (p*) models for social networks. Social networks (2007)."},{"key":"e_1_3_2_2_37_1","volume-title":"The map equation. The European Physical Journal Special Topics","author":"Rosvall Martin","year":"2009","unstructured":"Martin Rosvall , Daniel Axelsson , and Carl T Bergstrom . 2009. The map equation. The European Physical Journal Special Topics ( 2009 ). Martin Rosvall, Daniel Axelsson, and Carl T Bergstrom. 2009. The map equation. The European Physical Journal Special Topics (2009)."},{"key":"e_1_3_2_2_38_1","volume-title":"SIGKDD Workshop on Mining and Learning with Graphs .","author":"Shah Neil","year":"2020","unstructured":"Neil Shah . 2020 . Scale-Free, Attributed and Class-Assortative Graph Generation to Facilitate Introspection of Graph Neural Networks . In SIGKDD Workshop on Mining and Learning with Graphs . Neil Shah. 2020. Scale-Free, Attributed and Class-Assortative Graph Generation to Facilitate Introspection of Graph Neural Networks. In SIGKDD Workshop on Mining and Learning with Graphs ."},{"key":"e_1_3_2_2_39_1","volume-title":"Edgecentric: Anomaly detection in edge-attributed networks","author":"Shah Neil","year":"2016","unstructured":"Neil Shah , Alex Beutel , Bryan Hooi , Leman Akoglu , Stephan Gunnemann , Disha Makhija , Mohit Kumar , and Christos Faloutsos . 2016 . Edgecentric: Anomaly detection in edge-attributed networks . In ICDMW. IEEE. Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Gunnemann, Disha Makhija, Mohit Kumar, and Christos Faloutsos. 2016. Edgecentric: Anomaly detection in edge-attributed networks. In ICDMW. IEEE."},{"key":"e_1_3_2_2_40_1","volume-title":"The Infinity Mirror Test for Graph Models. arXiv preprint arXiv:2009.08925","author":"Sikdar Satyaki","year":"2020","unstructured":"Satyaki Sikdar , Daniel Gonzalez , Trenton Ford , and Tim Weninger . 2020. The Infinity Mirror Test for Graph Models. arXiv preprint arXiv:2009.08925 ( 2020 ). Satyaki Sikdar, Daniel Gonzalez, Trenton Ford, and Tim Weninger. 2020. The Infinity Mirror Test for Graph Models. arXiv preprint arXiv:2009.08925 (2020)."},{"key":"e_1_3_2_2_41_1","volume-title":"Modeling graphs with vertex replacement grammars","author":"Sikdar Satyaki","unstructured":"Satyaki Sikdar , Justus Hibshman , and Tim Weninger . 2019. Modeling graphs with vertex replacement grammars . In ICDM. IEEE. Satyaki Sikdar, Justus Hibshman, and Tim Weninger. 2019. Modeling graphs with vertex replacement grammars. In ICDM. IEEE."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01418-6_41"},{"key":"e_1_3_2_2_43_1","volume-title":"A fast kernel for attributed graphs","author":"Su Yu","unstructured":"Yu Su , Fangqiu Han , Richard E Harang , and Xifeng Yan . 2016. A fast kernel for attributed graphs . In SDM. SIAM , 486--494. Yu Su, Fangqiu Han, Richard E Harang, and Xifeng Yan. 2016. A fast kernel for attributed graphs. In SDM. SIAM, 486--494."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2481244.2481248"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402736"},{"key":"e_1_3_2_2_46_1","volume-title":"Efficient subgraph matching on billion node graphs. VLDB","author":"Sun Zhao","year":"2012","unstructured":"Zhao Sun , Hongzhi Wang , Haixun Wang , Bin Shao , and Jianzhong Li. 2012. Efficient subgraph matching on billion node graphs. VLDB ( 2012 ). Zhao Sun, Hongzhi Wang, Haixun Wang, Bin Shao, and Jianzhong Li. 2012. Efficient subgraph matching on billion node graphs. VLDB (2012)."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"crossref","unstructured":"Hanghang Tong Christos Faloutsos Brian Gallagher and Tina Eliassi-Rad. 2007. Fast best-effort pattern matching in large attributed graphs. In KDD . Hanghang Tong Christos Faloutsos Brian Gallagher and Tina Eliassi-Rad. 2007. Fast best-effort pattern matching in large attributed graphs. In KDD .","DOI":"10.1145\/1281192.1281271"},{"key":"e_1_3_2_2_49_1","volume-title":"From Louvain to","author":"Traag V. A.","year":"2019","unstructured":"V. A. Traag , L. Waltman , and N. J. van Eck . 2019. From Louvain to Leiden : guaranteeing well-connected communities. Scientific Reports ( 2019 ). V. A. Traag, L. Waltman, and N. J. van Eck. 2019. From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports (2019)."},{"key":"e_1_3_2_2_50_1","volume-title":"Structural diversity in social contagion. PNAS","author":"Ugander Johan","year":"2012","unstructured":"Johan Ugander , Lars Backstrom , Cameron Marlow , and Jon Kleinberg . 2012. Structural diversity in social contagion. PNAS ( 2012 ). Johan Ugander, Lars Backstrom, Cameron Marlow, and Jon Kleinberg. 2012. Structural diversity in social contagion. PNAS (2012)."},{"key":"e_1_3_2_2_51_1","volume-title":"Graph attention networks. ICLR","author":"Petar Velivc","year":"2018","unstructured":"Petar Velivc kovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2018. Graph attention networks. ICLR ( 2018 ). Petar Velivc kovi\u0107 , Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. ICLR (2018)."},{"key":"e_1_3_2_2_52_1","volume-title":"FANMOD: a tool for fast network motif detection. Bioinformatics","author":"Wernicke Sebastian","year":"2006","unstructured":"Sebastian Wernicke and Florian Rasche . 2006. FANMOD: a tool for fast network motif detection. Bioinformatics ( 2006 ). Sebastian Wernicke and Florian Rasche. 2006. FANMOD: a tool for fast network motif detection. Bioinformatics (2006)."},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2008.03.011"},{"key":"e_1_3_2_2_54_1","volume-title":"How powerful are graph neural networks? ICLR","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2019. How powerful are graph neural networks? ICLR ( 2019 ). Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? ICLR (2019)."},{"key":"e_1_3_2_2_55_1","volume-title":"gspan: Graph-based substructure pattern mining","author":"Yan Xifeng","unstructured":"Xifeng Yan and Jiawei Han . 2002. gspan: Graph-based substructure pattern mining . In ICDM. IEEE. Xifeng Yan and Jiawei Han. 2002. gspan: Graph-based substructure pattern mining. In ICDM. IEEE."},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"crossref","unstructured":"Carl Yang Xiaolin Shi Luo Jie and Jiawei Han. 2018. I know you'll be back: Interpretable new user clustering and churn prediction on a mobile social application. In KDD . Carl Yang Xiaolin Shi Luo Jie and Jiawei Han. 2018. I know you'll be back: Interpretable new user clustering and churn prediction on a mobile social application. In KDD .","DOI":"10.1145\/3219819.3219821"},{"key":"e_1_3_2_2_57_1","unstructured":"Jiaxuan You Rex Ying Xiang Ren William Hamilton and Jure Leskovec. 2018. GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models. In ICML . Jiaxuan You Rex Ying Xiang Ren William Hamilton and Jure Leskovec. 2018. GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models. In ICML ."},{"key":"e_1_3_2_2_58_1","volume-title":"Clustering large attributed graphs: An efficient incremental approach","author":"Zhou Yang","unstructured":"Yang Zhou , Hong Cheng , and Jeffrey Xu Yu. 2010. Clustering large attributed graphs: An efficient incremental approach . In ICDM. IEEE , 689--698. Yang Zhou, Hong Cheng, and Jeffrey Xu Yu. 2010. Clustering large attributed graphs: An efficient incremental approach. In ICDM. IEEE, 689--698."},{"key":"e_1_3_2_2_59_1","volume-title":"2020 a. Graph Neural Networks with Heterophily. arXiv preprint arXiv:2009.13566","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu , Ryan A Rossi , Anup Rao , Tung Mai , Nedim Lipka , Nesreen K Ahmed , and Danai Koutra . 2020 a. Graph Neural Networks with Heterophily. arXiv preprint arXiv:2009.13566 ( 2020 ). Jiong Zhu, Ryan A Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen K Ahmed, and Danai Koutra. 2020 a. Graph Neural Networks with Heterophily. arXiv preprint arXiv:2009.13566 (2020)."},{"key":"e_1_3_2_2_60_1","volume-title":"Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. Neurips","volume":"33","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu , Yujun Yan , Lingxiao Zhao , Mark Heimann , Leman Akoglu , and Danai Koutra . 2020 b . Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. Neurips , Vol. 33 (2020). Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020 b. Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. Neurips , Vol. 33 (2020)."}],"event":{"name":"WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining","location":"Virtual Event AZ USA","acronym":"WSDM '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3488560.3498492","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3488560.3498492","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3488560.3498492","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T01:12:03Z","timestamp":1669252323000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488560.3498492"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,11]]},"references-count":60,"alternative-id":["10.1145\/3488560.3498492","10.1145\/3488560"],"URL":"http:\/\/dx.doi.org\/10.1145\/3488560.3498492","relation":{},"published":{"date-parts":[[2022,2,11]]}}}