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Community detection is a fundamental task in network analysis. Applications on massive dynamic networks require more efficient solutions and lead to incremental community detection, which revises the community assignments of new or changed vertices during network updates. In this paper, we propose to use machine learning classifiers to predict the vertices that need to be inspected for community assignment revision. This learning-based targeted revision (LBTR) approach aims to improve community detection efficiency by filtering out the unchanged vertices from unnecessary processing. In this paper, we design features that can be used for efficient target classification and analyze the time complexity of our framework. We conduct experiments on two real-world datasets, which show our LBTR approach significantly reduces the computational time while keeping a high community detection quality. Furthermore, as compared with the benchmarks, we find our approach’s performance is stable on both growing networks and networks with vertex/edge removals. Experiments suggest that one should increase the target classification precision while keeping recall at a reasonable level when implementing our proposed approach. The study provides a unique perspective in incremental community detection. [ABSTRACT FROM AUTHOR]

Popularity prediction of online content over social media platforms is a valuable and challenging issue, the core of which lies in how to capture predictive factors from available data. However, existing studies either treat each cascade independently, which neglects the correlation among different cascades, or lack a comprehensive consideration of user behavioral proximity and preference with respect to different messages. Motivated by the above observation, this article proposes a graph neural network-based framework named HeDAN (heterogeneous diffusion attention network), which comprehensively considers various factors affecting information diffusion to provide more accurate prediction results. Specifically, we first construct a heterogeneous diffusion graph with two types of nodes (user and message) and three types of relations (friendship , interaction , and interest). Among them, friendship reflects the strength of social relationships between users, interaction reflects the behavioral proximity between users, and interest reflects user preference for information. Next, a graph neural network model with a hierarchical attention mechanism is proposed to learn from these relations. Specifically, at the node level, we utilize the graph attention network to learn the subgraph structure and generate the representations of users and messages under each specific relationship. At the semantic level, we distinguish the importance of different nodes in different relations via the multi-head self-attention mechanism and fuse them into the final prediction representation. Extensive experimental results on three real diffusion datasets show the superior performance of HeDAN over the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]