The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Edge Weight Prediction in Weighted Signed Networks
Abstract
Weighted signed networks (WSNs) are networks in which edges are labeled with positive and negative weights. WSNs can capture like/dislike, trust/distrust, and other social relationships between people. In this paper, we consider the problem of predicting the weights of edges in such networks. We propose two novel measures of node behavior: the goodness of a node intuitively captures how much this node is liked/trusted by other nodes, while the fairness of a node captures how fair the node is in rating other nodes' likeability or trust level. We provide axioms that these two notions need to satisfy and show that past work does not meet these requirements for WSNs. We provide a mutually recursive definition of these two concepts and prove that they converge to a unique solution in linear time. We use the two measures to predict the edge weight in WSNs. Furthermore, we show that when compared against several individual algorithms from both the signed and unsigned social network literature, our fairness and goodness metrics almost always have the best predictive power. We then use these as features in different multiple regression models and show that we can predict edge weights on 2 Bitcoin WSNs, an Epinions WSN, 2 WSNs derived from Wikipedia, and a WSN derived from Twitter with more accurate results than past work. Moreover, fairness and goodness metrics form the most significant feature for prediction in most (but not all) cases.
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Cited by
eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks
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Anomaly Detection in Dynamic Graphs via Transformer
Liu, Y., Pan, S., Wang, Y. G., Xiong, F., Wang, L., Chen, Q., & Lee, V. C. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3124061 · Cited by 169
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs
Cai, L., Chen, Z., Luo, C., Gui, J., Ni, J., Li, D., & Chen, H. (2021) · Proceedings of the 30th ACM International Conference on Information & Knowledge Management · doi:10.1145/3459637.3481955 · Cited by 146
Structural-temporal coupling anomaly detection with dynamic graph transformer
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Metadata
- Authors
- Kumar, S., Spezzano, F., Subrahmanian, V. S., & Faloutsos, C.
- Venue
- 2016 IEEE 16th International Conference on Data Mining (ICDM)
- Publisher
- IEEE
- Published
- 2016-12
- Pages
- 221-230
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2585835859
- Cited by
- 498
- References
- 34
- FWCI
- 36.75
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kumar, S., Spezzano, F., Subrahmanian, V. S., & Faloutsos, C. (2016). Edge Weight Prediction in Weighted Signed Networks. 2016 IEEE 16th International Conference on Data Mining (ICDM). https://doi.org/10.1109/icdm.2016.0033
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