ABSTRACT
Dynamic social interaction networks are an important abstraction to model time-stamped social interactions such as eye contact, speaking and listening between people. These networks typically contain informative while subtle patterns that reflect people’s social characters and relationship, and therefore attract the attentions of a lot of social scientists and computer scientists. Previous approaches on extracting those patterns primarily rely on sophisticated expert knowledge of psychology and social science, and the obtained features are often overly task-specific. More generic models based on representation learning of dynamic networks may be applied, but the unique properties of social interactions cause severe model mismatch and degenerate the quality of the obtained representations. Here we fill this gap by proposing a novel framework, termed TEmporal network-DIffusion Convolutional networks (TEDIC), for generic representation learning on dynamic social interaction networks. We make TEDIC a good fit by designing two components: 1) Adopt diffusion of node attributes over a combination of the original network and its complement to capture long-hop interactive patterns embedded in the behaviors of people making or avoiding contact; 2) Leverage temporal convolution networks with hierarchical set-pooling operation to flexibly extract patterns from different-length interactions scattered over a long time span. The design also endows TEDIC with certain self-explaining power. We evaluate TEDIC over five real datasets for four different social character prediction tasks including deception detection, dominance identification, nervousness detection and community detection. TEDIC not only consistently outperforms previous SOTA’s, but also provides two important pieces of social insight. In addition, it exhibits favorable societal characteristics by remaining unbiased to people from different regions. Our project website is: http://snap.stanford.edu/tedic/.
- Oya Aran and Daniel Gatica-Perez. 2013. One of a kind: Inferring personality impressions in meetings. In Proceedings of the 15th ACM on International conference on multimodal interaction. 11–18.Google ScholarDigital Library
- Chongyang Bai, Maksim Bolonkin, Judee Burgoon, Chao Chen, Norah Dunbar, Bharat Singh, VS Subrahmanian, and Zhe Wu. 2019. Automatic Long-Term Deception Detection in Group Interaction Videos. In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1600–1605.Google Scholar
- Chongyang Bai, Maksim Bolonkin, Srijan Kumar, Jure Leskovec, Judee Burgoon, Norah Dunbar, and VS Subrahmanian. 2019. Predicting dominance in multi-person videos. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. AAAI Press, 4643–4650.Google ScholarCross Ref
- Chongyang Bai, Srijan Kumar, Jure Leskovec, Miriam Metzger, Jay F. Nunamaker, and V. S. Subrahmanian. 2019. Predicting the Visual Focus of Attention in Multi-Person Discussion Videos. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, 4504–4510. https://doi.org/10.24963/ijcai.2019/626Google ScholarCross Ref
- Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency. 2016. Openface: an open source facial behavior analysis toolkit. In 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 1–10.Google ScholarCross Ref
- Cigdem Beyan, Francesca Capozzi, Cristina Becchio, and Vittorio Murino. 2017. Prediction of the leadership style of an emergent leader using audio and visual nonverbal features. IEEE Transactions on Multimedia 20, 2 (2017), 441–456.Google ScholarDigital Library
- Eunjoon Cho, Seth A Myers, and Jure Leskovec. 2011. Friendship and mobility: user movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. 1082–1090.Google ScholarDigital Library
- Fan Chung. 2007. The heat kernel as the pagerank of a graph. Proceedings of the National Academy of Sciences 104, 50(2007), 19735–19740.Google ScholarCross Ref
- Hanjun Dai, Yichen Wang, Rakshit Trivedi, and Le Song. 2016. Deep coevolutionary network: Embedding user and item features for recommendation. arXiv preprint arXiv:1609.03675(2016).Google Scholar
- Steven Davis and Paul Mermelstein. 1980. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE transactions on acoustics, speech, and signal processing 28, 4(1980), 357–366.Google ScholarCross Ref
- Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in neural information processing systems. 3844–3852.Google Scholar
- Sergey Demyanov, James Bailey, Kotagiri Ramamohanarao, and Christopher Leckie. 2015. Detection of deception in the mafia party game. In Proceedings of the 2015 ACM on International Conference on Multimodal Interaction. 335–342.Google ScholarDigital Library
- Sergey Demyanov, James Bailey, Kotagiri Ramamohanarao, and Christopher Leckie. 2015. Detection of Deception in the Mafia Party Game. In ACM ICMI.Google Scholar
- John F Dovidio and Steve L Ellyson. 1982. Decoding visual dominance: Attributions of power based on relative percentages of looking while speaking and looking while listening. Social Psychology Quarterly(1982), 106–113.Google Scholar
- Nathan Eagle, Alex Sandy Pentland, and David Lazer. 2009. Inferring friendship network structure by using mobile phone data. Proceedings of the national academy of sciences 106, 36(2009), 15274–15278.Google ScholarCross Ref
- Don Eskridge. 2012. The Resistance: Avalon. Indie Boards & Cards.Google Scholar
- Wenjie Fu, Le Song, and Eric P Xing. 2009. Dynamic mixed membership blockmodel for evolving networks. In Proceedings of the 26th annual international conference on machine learning. 329–336.Google ScholarDigital Library
- Mathieu Génois, Christian L Vestergaard, Julie Fournet, André Panisson, Isabelle Bonmarin, and Alain Barrat. 2015. Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers. Network Science 3, 3 (2015), 326–347.Google ScholarCross Ref
- G Giannakakis, Matthew Pediaditis, Dimitris Manousos, Eleni Kazantzaki, Franco Chiarugi, Panagiotis G Simos, Kostas Marias, and Manolis Tsiknakis. 2017. Stress and anxiety detection using facial cues from videos. Biomedical Signal Processing and Control 31 (2017), 89–101.Google ScholarCross Ref
- Palash Goyal, Sujit Rokka Chhetri, and Arquimedes Canedo. 2020. dyngraph2vec: Capturing network dynamics using dynamic graph representation learning. Knowledge-Based Systems 187 (2020), 104816.Google ScholarCross Ref
- Ehsan Hajiramezanali, Arman Hasanzadeh, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, and Xiaoning Qian. 2019. Variational graph recurrent neural networks. In Advances in Neural Information Processing Systems. 10700–10710.Google Scholar
- Dinesh Babu Jayagopi, Hayley Hung, Chuohao Yeo, and Daniel Gatica-Perez. 2009. Modeling dominance in group conversations using nonverbal activity cues. IEEE Transactions on Audio, Speech, and Language Processing 17, 3(2009), 501–513.Google ScholarCross Ref
- Di Jin, Sungchul Kim, Ryan A Rossi, and Danai Koutra. 2020. From Static to Dynamic Node Embeddings. arXiv preprint arXiv:2009.10017(2020).Google Scholar
- Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907(2016).Google Scholar
- Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann. 2018. Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997(2018).Google Scholar
- Srijan Kumar, Chongyang Bai, V.S. Subrahmanian, and Jure Leskovec. 2021. Deception Detection in Group Video Conversations using Dynamic Interaction Networks. In Proceedings of the International AAAI Conference on Web and Social Media.Google Scholar
- Srijan Kumar, Xikun Zhang, and Jure Leskovec. 2019. Predicting dynamic embedding trajectory in temporal interaction networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1269–1278.Google ScholarDigital Library
- David Lazer, Alex Pentland, Lada Adamic, Sinan Aral, Albert-László Barabási, Devon Brewer, Nicholas Christakis, Noshir Contractor, James Fowler, Myron Gutmann, 2009. Computational social science. Science 323, 5915 (2009), 721–723.Google Scholar
- Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015), 436–444.Google Scholar
- Pan Li, I Chien, and Olgica Milenkovic. 2019. Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection. In Advances in Neural Information Processing Systems. 11705–11716.Google Scholar
- Yozen Liu, Xiaolin Shi, Lucas Pierce, and Xiang Ren. 2019. Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchat. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2023–2031.Google ScholarDigital Library
- Franco Manessi, Alessandro Rozza, and Mario Manzo. 2020. Dynamic graph convolutional networks. Pattern Recognition 97(2020), 107000.Google ScholarDigital Library
- Giang Hoang Nguyen, John Boaz Lee, Ryan A Rossi, Nesreen K Ahmed, Eunyee Koh, and Sungchul Kim. 2018. Continuous-time dynamic network embeddings. In Companion Proceedings of the The Web Conference 2018. 969–976.Google ScholarDigital Library
- Shogo Okada, Laurent Son Nguyen, Oya Aran, and Daniel Gatica-Perez. 2019. Modeling dyadic and group impressions with intermodal and interperson features. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 15, 1s (2019), 1–30.Google ScholarDigital Library
- Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The pagerank citation ranking: Bringing order to the web.Technical Report. Stanford InfoLab.Google Scholar
- Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, and Charles E Leisersen. 2019. Evolvegcn: Evolving graph convolutional networks for dynamic graphs. arXiv preprint arXiv:1902.10191(2019).Google Scholar
- Keith Rayner. 1998. Eye movements in reading and information processing: 20 years of research.Psychological bulletin 124, 3 (1998), 372.Google Scholar
- Ryan A Rossi, Brian Gallagher, Jennifer Neville, and Keith Henderson. 2013. Modeling dynamic behavior in large evolving graphs. In Proceedings of the sixth ACM international conference on Web search and data mining. 667–676.Google ScholarDigital Library
- Rudolph J Rummel. 1976. Understanding conflict and war: vol. 2: the conflict helix. Bev-erly Hills: Sage(1976).Google Scholar
- Dairazalia Sanchez-Cortes, Oya Aran, Marianne Schmid Mast, and Daniel Gatica-Perez. 2011. A nonverbal behavior approach to identify emergent leaders in small groups. IEEE Transactions on Multimedia 14, 3 (2011), 816–832. Dataset: https://www.idiap.ch/dataset/elea.Google ScholarDigital Library
- Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, and Hao Yang. 2020. DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks. In Proceedings of the 13th International Conference on Web Search and Data Mining. 519–527.Google ScholarDigital Library
- Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, and Xavier Bresson. 2018. Structured sequence modeling with graph convolutional recurrent networks. In International Conference on Neural Information Processing. Springer, 362–373.Google ScholarDigital Library
- Sucheta Soundarajan, Acar Tamersoy, Elias B Khalil, Tina Eliassi-Rad, Duen Horng Chau, Brian Gallagher, and Kevin Roundy. 2016. Generating graph snapshots from streaming edge data. In Proceedings of the 25th International Conference Companion on World Wide Web. 109–110.Google ScholarDigital Library
- Aynaz Taheri, Kevin Gimpel, and Tanya Berger-Wolf. 2019. Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models. In Companion Proceedings of The 2019 World Wide Web Conference. 301–307.Google ScholarDigital Library
- Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, and Hongyuan Zha. 2019. DyRep: Learning Representations over Dynamic Graphs. In International Conference on Learning Representations.Google Scholar
- Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903(2017).Google Scholar
- Aldert Vrij. 2008. Detecting lies and deceit: Pitfalls and opportunities. John Wiley & Sons.Google Scholar
- Fred O Walumbwa and John Schaubroeck. 2009. Leader personality traits and employee voice behavior: mediating roles of ethical leadership and work group psychological safety.Journal of applied psychology 94, 5 (2009), 1275.Google Scholar
- Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr, Christopher Fifty, Tao Yu, and Kilian Q Weinberger. 2019. Simplifying graph convolutional networks. arXiv preprint arXiv:1902.07153(2019).Google Scholar
- Zhe Wu, Bharat Singh, Larry S Davis, and VS Subrahmanian. 2018. Deception detection in videos. In Thirty-Second AAAI Conference on Artificial Intelligence.Google ScholarCross Ref
- Eric P Xing, Wenjie Fu, Le Song, 2010. A state-space mixed membership blockmodel for dynamic network tomography. Annals of Applied Statistics 4, 2 (2010), 535–566.Google ScholarCross Ref
- Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. Gnnexplainer: Generating explanations for graph neural networks. In Advances in Neural Information Processing Systems. 9240–9251.Google Scholar
- Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola. 2017. Deep sets. In Advances in neural information processing systems. 3391–3401.Google Scholar
- Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang. 2018. Dynamic network embedding by modeling triadic closure process. In Thirty-Second AAAI Conference on Artificial Intelligence.Google ScholarCross Ref
- Yuan Zuo, Guannan Liu, Hao Lin, Jia Guo, Xiaoqian Hu, and Junjie Wu. 2018. Embedding temporal network via neighborhood formation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2857–2866.Google ScholarDigital Library
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