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As event-based social networks (EBSNs) such as Meetup.com and Facebook Events gain popularity in managing local events (e.g., farmers’ markets and social gatherings), two-sided cultural niches are created as event organizers and participants benefit from the platform while affecting each other. Among various factors, niche overlap, an ecological feature, has been studied as a key factor that shapes the success of online communities. While such ecological factors may also shape EBSN-based local groups’ success, the context of EBSNs raises unique challenges in understanding the roles of cultural niches due to the informal nature of the local groups and their geographical embeddedness. In this paper, we examine the effects of Meetup groups’ topic overlap and geospatial correlation on the activity levels of both organizers and participants, using one-year Meetup data for 500 cities in the United States. We find that (1) a group’s topic overlap with other groups on EBSN is associated with its activity levels, and (2) local groups’ geospatial correlation may moderate the effects of topic overlap for EBSN users, but inconsistently. The results provide a baseline understanding of EBSN-based groups from an ecological perspective. 

Graph synthesis is a long-standing research problem. Many deep neural networks that learn about latent characteristics of graphs and generate fake graphs have been proposed. However, in many cases their scalability is too high to be used to synthesize large graphs. Recently, one work proposed an interesting scalable idea to learn and generate random walks that can be merged into a graph. Due to its difficulty, however, the random walk-based graph synthesis failed to show state-of-the-art performance in many cases. We present an improved random walk-based method by using negative random walks. In our experiments with 6 datasets and 8 baseline methods, our method shows the best performance in almost all cases. We achieve both high scalability and generation quality. 

We present a prediction model to detect delayed graduation cases based on student network analysis. In the U.S. only 60% of undergraduate students finish their bachelors’ degrees in 6 years [1]. We present many features based on student networks and activity records. To our knowledge, our feature design, which includes conventional academic performance features, student network features, and fix-point features, is one of the most comprehensive ones. We achieved the F-1 score of 0.85 and AUCROC of 0.86.