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1. An Agent-Based Model of Common Knowledge and Collective Action Dynamics on Social Networks

   https://doi.org/10.1109/WSC48552.2020.9383995  

   Kuhlman, Chris J.; Ravi, S. S.; Korkmaz, Gizem; Vega-Redondo, Fernando (December 2020, 2020 Winter Simulation Conference (WSC))

   null (Ed.)

   Protest is a collective action problem and can be modeled as a coordination game in which people take an action with the potential to achieve shared mutual benefits. In game-theoretic contexts, successful coordination requires that people know each others' willingness to participate, and that this information is common knowledge among a sufficient number of people. We develop an agent-based model of collective action that was the first to combine social structure and individual incentives. Another novel aspect of the model is that a social network increases in density (i.e., new graph edges are formed) over time. The model studies the formation of common knowledge through local interactions and the characterizing social network structures. We use four real-world, data-mined social networks (Facebook, Wikipedia, email, and peer-to-peer networks) and one scale-free network, and conduct computational experiments to study contagion dynamics under different conditions. 

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2. An Agent-Based Model of Common Knowledge and Collective Action Dynamics on Social Networks

   Kuhlman, Chris J; Korkmaz, Gizem; Ravi, SS; Vega-Redondo, Fernando. (January 2020, Proceedings of the Winter Simulation Conference)

   Bae, K-H; Feng, B; Kim, S; Lazarova-Molnar, S; Zheng, Z; Roeder, T; Thiesing, R. (Ed.)

   Protest is a collective action problem and can be modeled as a coordination game in which people take an action with the potential to achieve shared mutual benefits. In game-theoretic contexts, successful coordination requires that people know each others’ willingness to participate, and that this information is common knowledge among a sufficient number of people. We develop an agent-based model of collective action that was the first to combine social structure and individual incentives. Another novel aspect of the model is that a social network increases in density (i.e., new graph edges are formed) over time. The model studies the formation of common knowledge through local interactions and the characterizing social network structures. We use four real-world, data-mined social networks (Facebook, Wikipedia, email, and peer-to-peer networks) and one scale-free network, and conduct computational experiments to study contagion dynamics under different conditions. 

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3. Influence Propagation with Multiple Stages over Random Multiplex Networks

   https://doi.org/10.1109/GLOBECOM38437.2019.9013531  

   Zhuang, Yong; Yagan, Osman (December 2019, 2019 IEEE Global Communications Conference (GLOBECOM))

   Complex contagion models have been developed to understand a wide range of social phenomena such as adoption of cultural fads, the diffusion of belief, norms, and innovations in social networks, and the rise of collective action to join a riot. Most existing works focus on contagions where individuals’ states are represented by binary variables, and propagation takes place over a single isolated network. However, characterization of an individual’s standing on a given matter as a binary state might be overly simplistic as most of our opinions, feelings, and perceptions vary over more than two states. Also, most real-world contagions take place over multiple networks (e.g., Twitter and Facebook) or involve multiplex networks where individuals engage in different types of relationships (e.g., co-worker, family, etc.). To this end, this paper studies multi-stage complex contagions that take place over multi-layer or multiplex networks. Under a linear threshold based contagion model, we first give analytic results for the expected size of global cascades, i.e., cases where a randomly chosen node can initiate a propagation that eventually reaches a positive fraction of the whole population. Then, analytic results are confirmed by an extensive numerical study. In addition, we demonstrate how the dynamics of complex contagions is affected by the structural properties of the networks. In particular, we reveal an interesting connection between the assortativity of a network and the impact of hyper-active nodes on the cascade size. 

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4. Learning Common Knowledge Networks Via Exponential Random Graph Models

   Liu, Xueying; Hu, Zhihao; Deng, Xinwei; Kuhlman, Chris J. (January 2024, Proceedings of the International Conference on Advances in Social Network Analysis and Mining)

   Common knowledge (CK) is a phenomenon where each individual within a group knows the same information and everyone knows that everyone knows the information, infinitely recursively. CK spreads information as a contagion through social networks in ways different from other models like susceptible-infectious-recovered (SIR) model. In a model of CK on Facebook, the biclique serves as the characterizing graph substructure for generating CK, as all nodes within a biclique share CK through their walls. To understand the effects of network structure on CK-based contagion, it is necessary to control the numbers and sizes of bicliques in networks. Thus, learning how to generate these CK networks (CKNs) is important. Consequently, we develop an exponential random graph model (ERGM) that constructs networks while controlling for bicliques. Our method offers powerful prediction and inference, reduces computational costs significantly, and has proven its merit in contagion dynamics through numerical experiments. 

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5. Contrastive Attributed Network Anomaly Detection with Data Augmentation

   https://doi.org/10.1007/978-3-031-05936-0_35  

   Xu, Zhiming; Huang, Xiao; Zhao, Yue; Dong, Yushun; Li, Jundong (July 2022, Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022)

   Attributed networks are a type of graph structured data used in many real-world scenarios. Detecting anomalies on attributed networks has a wide spectrum of applications such as spammer detection and fraud detection. Although this research area draws increasing attention in the last few years, previous works are mostly unsupervised because of expensive costs of labeling ground truth anomalies. Many recent studies have shown different types of anomalies are often mixed together on attributed networks and such invaluable human knowledge could provide complementary insights in advancing anomaly detection on attributed networks. To this end, we study the novel problem of modeling and integrating human knowledge of different anomaly types for attributed network anomaly detection. Specifically, we first model prior human knowledge through a novel data augmentation strategy. We then integrate the modeled knowledge in a Siamese graph neural network encoder through a well-designed contrastive loss. In the end, we train a decoder to reconstruct the original networks from the node representations learned by the encoder, and rank nodes according to its reconstruction error as the anomaly metric. Experiments on five real-world datasets demonstrate that the proposed framework outperforms the state-of-the-art anomaly detection algorithms. 

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