Traditional social learning frameworks consider environments with a homogeneous state where each agent receives observations conditioned on the same hypothesis. In this work, we study the distributed hypothesis testing problem for graphs with a community structure, assuming that each cluster receives data conditioned on some different true state. This situation arises in many scenarios, such as when sensors are spatially distributed, or when individuals in a social network have differing views or opinions. We show that the adaptive social learning strategy is not only superior in nonstationary environments, but also allows each cluster to discover its own truth.

Non-Bayesian social learning is a framework for distributed hypothesis testing aimed at learning the true state of the environment. Traditionally, the agents are assumed to receive observations conditioned on the same true state, although it is also possible to examine the case of heterogeneous models across the graph. One important special case is when heterogeneity is caused by the presence of malicious agents whose goal is to move the agents toward a wrong hypothesis. In this letter, we propose an algorithm that allows discovering the true state of every individual agent based on thesequenceof their beliefs. In so doing, the methodology is also able to locate malicious behavior.

Social learning algorithms provide a model for the formation and propagation of opinions over social networks. However, most studies focus on the case in which agents share their information synchronously over regular intervals. In this work, we analyze belief convergence and steady-state learning performance for both traditional and adaptive formulations of social learning under asynchronous behavior by the agents, where some of the agents may decide to abstain from sharing any information with the network at some time instants. We also show how to recover the underlying graph topology from observations of the asynchronous network behavior.

The adaptive social learning paradigm deals with the opinion formation process by a network of communicating agents in a dynamic environment. In this study, we show that a sequence of publicly exchanged beliefs allows users to discover rich information about the underlying model. In particular, it is shown that it is possible (i) to identify the influence of each individual agent to the objective of truth learning, (ii) to discover how well-informed each agent is, and (iii) to learn the underlying network topology.

Each day bank clients conduct numerous operations, such as purchasing goods or transferring money to other clients. These interactions can be interpreted as a graph dynamically changing over time. This work focuses on the task of predicting new interactions in the network of bank clients and treats it as a link prediction problem. We propose an architecture for the graph convolutional network to efficiently solve the link prediction problem for this type of data. Our model uses recurrent neural networks to leverage the time-series data in both nodes and edges and effectively scales to the graphs with millions of nodes. We evaluate the model on the data provided for several years by a large European bank. The obtained results show that the model outperforms the existing approaches. The current paper is an extended abstract for the work [5].

In this paper, we discuss how modern deep learning approaches can be applied to the credit scoring of bank clients. We show that information about connections between clients based on money transfers between them allows us to significantly improve the quality of credit scoring compared to the approaches using information about the target client solely. As a final solution, we develop a new graph neural network model EWS-GCN that combines ideas of graph convolutional and recurrent neural networks via attention mechanism. The resulting model allows for robust training and efficient processing of large-scale data. We also demonstrate that our model outperforms the state-of-the-art graph neural networks achieving excellent results.