Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding
Article No.: 219, Pages 1 - 26
Abstract
The problem of representing nodes in a signed network as low-dimensional vectors, known as signed network embedding (SNE), has garnered considerable attention in recent years. While several SNE methods based on graph convolutional networks (GCNs) have been proposed for this problem, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real-world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which corrects for incorrect embedding propagation in GCN by utilizing the trustworthiness on edge signs for high-order relationships inferred by the balance theory. The proposed approach consists of three modules: (M1) generation of each node’s extended ego-network; (M2) measurement of trustworthiness on edge signs; and (M3) trustworthiness-aware propagation of embeddings. Specifically, TrustSGCN leverages topological information to measure trustworthiness on edge sign for high-order relationships inferred by balance theory. It then considers structural properties inherent to an input network, such as the ratio of triads, to correct for incorrect embedding propagation. Furthermore, TrustSGCN learns the node embeddings by leveraging two well-known social theories, i.e., balance and status, to jointly preserve the edge sign and direction between nodes connected by existing edges in the embedding space. The experiments on six real-world signed network datasets demonstrate that TrustSGCN consistently outperforms six state-of-the-art GCN-based SNE methods. The code is available at https://github.com/kmj0792/TrustSGCN.
References
[1]
Teresa Alsinet, Josep Argelich, Ramón Béjar, and Santi Martínez. 2021. Measuring Polarization in Online Debates. Applied Sciences 11, 24 (2021), 11879. DOI:
[2]
Omid Askarisichani, JacquelineNg Lane, Francesco Bullo, NoahE Friedkin, AmbujK Singh, and Brian Uzzi. 2019. Structural balance emerges and explains performance in risky decision-making. Nat. Commun. 10, 1 (Jun 2019), 2648. DOI:
[3]
Hong-Kyun Bae, Hae-Ri Jang, Yang-Sae Moon, and Sang-Wook Kim. 2024. Item-ranking promotion in recommender systems. In Companion Proceedings of the ACM on Web Conference 2024 (WWW ’24), 505–508.
[4]
Kamal Berahmand, Fatemeh Daneshfar, Elaheh Sadat Salehi, Yuefeng Li, and Yue Xu. 2024. Autoencoders and their applications in machine learning: A survey. Artificial Intelligence Review 57, 2 (2024), 28.
[5]
Kamal Berahmand, Yuefeng Li, and Yue Xu. 2024. A Deep Semi-Supervised Community Detection Based on Point-Wise Mutual Information. IEEE Trans. Comput. Soc. Syst. 11, 3 (2024), 3444–3456. DOI:
[6]
Brigitte Boden, Stephan Günnemann, Holger Hoffmann, and Thomas Seidl. 2012. Mining coherent subgraphs in multi-layer graphs with edge labels. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’12), 1258–1266.
[7]
Francesco Bonchi, Edoardo Galimberti, Aristides Gionis, Bruno Ordozgoiti, and Giancarlo Ruffo. 2019. Discovering polarized communities in signed networks. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM ’19), 961–970.
[8]
Dorwin Cartwright and Frank Harary. 1956. Structural balance: A generalization of Heider’s theory. Psychological Review 63, 5 (1956), 277.
[9]
Jie Chen, Tengfei Ma, and Cao Xiao. 2018. FastGCN: Fast learning with graph convolutional networks via importance sampling. arXiv:1801.10247.
[10]
Yiqi Chen, Tieyun Qian, Huan Liu, and Ke Sun. 2018. “Bridge”: Enhanced signed directed network embedding. In Proceedings of the International Conference on Information and Knowledge Management (CIKM ’18), 773–782.
[11]
Quang-Vinh Dang and Claudia-Lavinia Ignat. 2018. Link-sign prediction in dynamic signed directed networks. In Proceedings of the IEEE 4th International Conference on Collaboration and Internet Computing (CIC ’18), 36–45.
[12]
Tyler Derr, Yao Ma, and Jiliang Tang. 2018. Signed graph convolutional networks. In Proceedings of the IEEE International Conference on Data Mining (ICDM ’18), 929–934.
[13]
Patrick Doreian and Andrej Mrvar. 2015. Structural balance and signed international relations. Journal of Social Structure 16, 1 (2015), 2.
[14]
Ernesto Estrada. 2019. Rethinking structural balance in signed social networks. Discrete Applied Mathematics 268 (2019), 70–90.
[15]
Ming-Han Feng, Chin-Chi Hsu, Cheng-Te Li, Mi-Yen Yeh, and Shou-De Lin. 2019. Marine: Multi-relational network embeddings with relational proximity and node attributes. In Proceedings of the World Wide Web Conference, 470–479.
[16]
Stefano Fiorini, Stefano Coniglio, Michele Ciavotta, and Enza Messina. 2023. Sigmanet: One laplacian to rule them all. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 7568–7576.
[17]
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the Advances in Neural Information Processing Systems 30 (2017), 1025–1035.
[18]
J. A. Hanley, and B. J. McNeil. 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 1 (Apr 1982), 29–36. DOI:.
[19]
Fritz Heider. 1946. Attitudes and cognitive organization. The Journal of Psychology 21, 1 (1946), 107–112.
[20]
Haojun Huang, Fengxiang Ding, Hao Yin, Gaoyang Liu, Chen Wang, and Dapeng Oliver Wu. 2024. EgoMUIL: Enhancing spatio-temporal user identity linkage in location-based social networks with Ego-Mo hypergraph. IEEE Transactions on Mobile Computing 23, 8 (2024), 8341–8354.
[21]
Junjie Huang, Huawei Shen, Liang Hou, and Xueqi Cheng. 2019. Signed graph attention networks. In Proceedings of the International Conference on Artificial Neural Networks (ICANN ’19), Vol. 11731, 566–577.
[22]
Junjie Huang, Huawei Shen, Liang Hou, and Xueqi Cheng. 2021. SDGNN: Learning node representation for signed directed networks. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI ’21), 196–203.
[23]
Zexi Huang, Arlei Silva, and Ambuj K. Singh. 2022. POLE: Polarized embedding for signed networks. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM ’22), 390–400.
[24]
Bei Hui, Lizong Zhang, Xue Zhou, Xiao Wen, and Yuhui Nian. 2022. Personalized recommendation system based on knowledge embedding and historical behavior. Applied Intelligence 52, 1 (2022), 954–966.
[25]
Won-Seok Hwang, Juan Parc, Sang-Wook Kim, Jongwuk Lee, and Dongwon Lee. 2016. ”Told you i didn’t like it”: Exploiting uninteresting items for effective collaborative filtering. In Proceedings of the IEEE International Conference on Data Engineering (ICDE ’16), 349–360.
[26]
Vijay Ingalalli, Dino Ienco, and Pascal Poncelet. 2018. Mining frequent subgraphs in multigraphs. Information Sciences 451–452 (2018), 50–66.
[27]
David Y. Kang, Woncheol Lee, Yeon-Chang Lee, Kyungsik Han, and Sang-Wook Kim. 2022. A framework for accurate community detection on signed networks using adversarial learning. IEEE Transactions on Knowledge and Data Engineering 35, 11 (2022), 10937–10951.
[28]
Yoonsuk Kang, Woncheol Lee, Yeon-Chang Lee, Kyungsik Han, and Sang-Wook Kim. 2021. Adversarial learning of balanced triangles for accurate community detection on signed networks. In Proceedings of the IEEE International Conference on Data Mining (ICDM ’21). IEEE, 1150–1155.
[29]
Junghwan Kim, Haekyu Park, Ji-Eun Lee, and U. Kang. 2018. Side: Representation learning in signed directed networks. In Proceedings of the 2018 World Wide Web Conference, 509–518.
[30]
Min-Jeong Kim, Yeon-Chang Lee, and Sang-Wook Kim. 2023. TrustSGCN: Learning trustworthiness on edge signs for effective signed graph convolutional networks. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23).
[31]
Taeri Kim, Jiho Heo, Hongil Kim, Kijung Shin, and Sang-Wook Kim. 2024a. VITA: ’Carefully chosen and weighted less’ is better in medication recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, 8600–8607.
[32]
Taeho Kim, Juwon Yu, Won-Yong Shin, Hyunyoung Lee, Ji-Hui Im, and Sang-Wook Kim. 2023. LATTE: A framework for learning item-features to make a domain-expert for effective conversational recommendation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’23). ACM, 1144–1153.
[33]
Yungi Kim, Taeri Kim, Won-Yong Shin, and Sang-Wook Kim. 2024. MONET: Modality-embracing graph convolutional network and target-aware attention for multimedia recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 332–340.
[34]
Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR ’17).
[35]
Yun-Yong Ko, Seongeun Ryu, Soeun Han, Youngseung Jeon, Jaehoon Kim, Sohyun Park, Kyungsik Han, Hanghang Tong, and Sang-Wook Kim. 2023. KHAN: Knowledge-aware hierarchical attention networks for accurate political stance prediction. In Proceedings of the ACM Web Conference (WWW ’23). ACM, 1572–1583.
[36]
Jérôme Kunegis. 2013. Konect: The koblenz network collection. In Proceedings of the 22nd International Conference on World Wide Web, 1343–1350.
[37]
Wonchang Lee, Yeon-Chang Lee, Dongwon Lee, and Sang-Wook Kim. 2021. Look before you leap: Confirming edge signs in random walk with restart for personalized node ranking in signed networks. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’21), 143–152.
[38]
Yeon-Chang Lee, Nayoun Seo, Kyungsik Han, and Sang-Wook Kim. 2020. ASiNE: Adversarial signed network embedding. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’20), 609–618.
[39]
Yeon-Chang Lee, Sang-Wook Kim, and Dongwon Lee. 2018. gOCCF: Graph-theoretic one-class collaborative filtering based on uninteresting items. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI ’18), 3448–3456.
[40]
Yeon-Chang Lee, JaeHyun Lee, Dongwon Lee, and Sang-Wook Kim. 2022. THOR: Self-supervised temporal knowledge graph embedding via three-tower graph convolutional networks. In Proceedings of the IEEE International Conference on Data Mining (ICDM ’22), 1035–1040.
[41]
Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. 2010. Signed networks in social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1361–1370.
[42]
Jure Leskovec, Daniel P. Huttenlocher, and Jon M. Kleinberg. 2010. Predicting positive and negative links in online social networks. In Proceedings of the ACM Web Conference (WWW), 641–650.
[43]
Jiaye Li, Jian Zhang, Jilian Zhang, and Shichao Zhang. 2024. Quantum KNN Classification With K Value Selection and Neighbor Selection. IEEE Trans. Comput-Aided. Des. Integr. Circuits Syst. 43, 5 (2024), 1332–1345. DOI:
[44]
Xiaoming Li, Hui Fang, and Jie Zhang. 2017. Rethinking the link prediction problem in signed social networks. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI ’17), 4955–4956.
[45]
Yu Li, Yuan Tian, Jiawei Zhang, and Yi Chang. 2020. Learning signed network embedding via graph attention. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI ’20), 4772–4779.
[46]
Haoxin Liu. 2022. LightSGCN: Powering signed graph convolution network for link sign prediction with simplified architecture design. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2680–2685.
[47]
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016. Learning convolutional neural networks for graphs. In Proceedings of the International Conference on Machine Learning (ICML ’16), Vol. 48, 2014–2023.
[48]
Jeong-Min Park, Myung-Hwan Jang, Duck-Ho Bae, and Sang-Wook Kim. 2024. RealGraphGPU++: A high-performance GPU-based graph engine with direct storage-to-DM IO. In Companion Proceedings of the ACM on Web Conference 2024, 654–657.
[49]
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. 2020. Temporal graph networks for deep learning on dynamic graphs. In Proceedings of the International Conference on Machine Learning (ICML ’20).
[50]
Kartik Sharma, Mohit Raghavendra, Yeon-Chang Lee, and Srijan Kumar. 2023. Representation learning in continuous-time dynamic signed networks. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2229–2238.
[51]
Lin Shu, Erxin Du, Yaomin Chang, Chuan Chen, Zibin Zheng, Xingxing Xing, and Shaofeng Shen. 2021. SGCL: Contrastive representation learning for signed graphs. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM ’21), 1671–1680.
[52]
Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, Soo-Hyun Choi, and Xia Hu. 2023. Bring your own view: Graph neural networks for link prediction with personalized subgraph selection. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining, 625–633.
[53]
Jiliang Tang, Charu C. Aggarwal, and Huan Liu. 2016. Recommendations in signed social networks. In Proceedings of the ACM Web Conference (WWW ’16), 31–40.
[54]
Jiliang Tang, Yi Chang, Charu C. Aggarwal, and Huan Liu. 2016. A survey of signed network mining in social media. ACM Computing Surveys 49, 3 (2016), 42:1–42:37.
[55]
Matt Thomas, Bo Pang, and Lillian Lee. 2006. Get the out vote: Determining support or opposition from congressional floor-debate transcripts. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 327–335.
[56]
Fatemeh Vahedian, Robin Burke, and Bamshad Mobasher. 2017. Weighted random walk sampling for multi-relational recommendation. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, 230–237.
[57]
Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, and Qi Liu. 2018. SHINE: Signed heterogeneous information network embedding for sentiment link prediction. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM ’18), 592–600.
[58]
Suhang Wang, Jiliang Tang, Charu Aggarwal, Yi Chang, and Huan Liu. 2017. Signed network embedding in social media. In Proceedings of the 2017 SIAM International Conference on Data Mining, 327–335.
[59]
Suhang Wang, Jiliang Tang, Charu C. Aggarwal, Yi Chang, and Huan Liu. 2017. Signed network embedding in social media. In Proceedings of the SIAM International Conference on Data Mining (SDM ’17), 327–335.
[60]
Yanbang Wang, Yen-Yu Chang, Yunyu Liu, Jure Leskovec, and Pan Li. 2021. Inductive representation learning in temporal networks via causal anonymous walks. In Proceedings of the International Conference on Learning Representations (ICLR ’21).
[61]
Pinghua Xu, Yibing Zhan, Liu Liu, Baosheng Yu, Bo Du, Jia Wu, and Wenbin Hu. 2022. Dual-branch density ratio estimation for signed network embedding. In Proceedings of the ACM Web Conference (WWW ’22), 1651–1662.
[62]
Hyunsik Yoo, Yeon-Chang Lee, Kijung Shin, and Sang-Wook Kim. 2022. Directed network embedding with virtual negative edges. In Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM ’22), 1291–1299.
[63]
Shuhan Yuan, Xintao Wu, and Yang Xiang. 2017. SNE: Signed network embedding. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’17), 183–195.
[64]
Guixian Zhang, Debo Cheng, Guan Yuan, and Shichao Zhang. 2024. Learning fair representations via rebalancing graph structure. Information Processing & Management 61, 1 (2024), 103570.
[65]
Guixian Zhang, Shichao Zhang, and Guan Yuan. 2024. Bayesian graph local extrema convolution with long-tail strategy for misinformation detection. ACM Transactions on Knowledge Discovery from Data 18, 4 (2024), 1–21.
[66]
He. Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, and Jian Pei. 2024. Trustworthy Graph Neural Networks: Aspects, Methods, and Trends. Proc. IEEE. 112, 2 (2024), 97–139. DOI:
[67]
Shichao Zhang and Jiaye Li. 2021. KNN classification with one-step computation. IEEE Transactions on Knowledge and Data Engineering 35, 3 (2021), 2711–2723.
[68]
Shichao Zhang, Jiaye Li, and Yangding Li. 2022. Reachable distance function for KNN classification. IEEE Transactions on Knowledge and Data Engineering 35, 7 (2022), 7382–7396.
[69]
Shichao Zhang, Jiaye Li, Wenzhen Zhang, and Yongsong Qin. 2022. Hyper-class representation of data. Neurocomputing 503 (2022), 200–218.
Index Terms
- Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding
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TrustSGCN: Learning Trustworthiness on Edge Signs for Effective Signed Graph Convolutional Networks
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalThe problem of signed network embedding (SNE) aims to represent nodes in a given signed network as low-dimensional vectors. While several SNE methods based on graph convolutional networks (GCN) have been proposed, we point out that they significantly ...
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Publication History
Published: 14 October 2024
Online AM: 06 August 2024
Accepted: 16 July 2024
Revised: 13 June 2024
Received: 23 January 2024
Published in TKDD Volume 18, Issue 9
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