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    推荐学术论文

    SCHOLAT开放数据

    Anomaly Detection on Attributed Network

    Community is the implicit structure in social networks. In academic social networks, the users with similar or same research interests are more likely to be in the same community with close links and similar attributes. Effective community detection results can be further utilized for user analytics and user recommendation.

    User-Generated Item Recommendation

    Anomaly detection on attributed networks is an important task in social network analysis. The goal is to find the anomalies that deviate significantly from the majority of the network in terms of some proximities, e.g. topological structure or attribute proximity. An effective anomaly detection can support many applications such as web spam detection, system fraud detection, network intrusion detection and representation learning.

    SCHOLAT Interactive User Recommendation

    Most of the existing recommendation methods assume that all the items are provided by separate producers, which is however not true in some recommendation tasks. That is, it is possible that some of the items are generated by users. Appropriately considering the user-item generation relation may bring benefit to some recommender systems, e.g., implicit recommender systems with only implicit user-item interactions.

    SCHOLAT ScholarNet

    The SCHOLAT Multiplex Network provides a comprehensive list of social information. In this network, we construct a multiplex structure with three layers: (1) The first layer represents connections between users who become friends. (2) The second layer represents connections between users who join the same groups. (3) The third layer represents connections between users who study the same courses. Furthermore, we define an individual ground-truth community based on the affiliation of users. All layers consist of the same 2,302 nodes with the highest quality. Each layer has a specific number of edges: 11,393 for the first layer, 139,004 for the second layer, and 70,226 for the third layer. We have divided these nodes into 11 communities.

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