Community Detection in Attributed Graphs: An Embedding Approach

Community detection is a fundamental and widely-studied problem that finds all densely-connected groups of nodes and well separates them from others in graphs. With the proliferation of rich information available for entities in real-world networks, it is useful to discover communities in attributed graphs where nodes tend to have attributes. However, most existing attributed community detection methods directly utilize the original network topology leading to poor results due to ignoring inherent community structures. In this paper, we propose a novel embedding based model to discover communities in attributed graphs. Specifically, based on the observation of densely-connected structures in communities, we develop a novel community structure embedding method to encode inherent community structures via underlying community memberships. Based on node attributes and community structure embedding, we formulate the attributed community detection as a nonnegative matrix factorization optimization problem. Moreover, we carefully design iterative updating rules to make sure of finding a converging solution. Extensive experiments conducted on 19 attributed graph datasets with overlapping and non-overlapping ground-truth communities show that our proposed model CDE can accurately identify attributed communities and significantly outperform 7 state-of-the-art methods

Dynamically Maintaining Frequent Items over A Data Stream

It is challenge to maintain frequent items over a data stream, with a small bounded memory, in a dynamic environment where both insertion/deletion of items are allowed. In this paper, we propose a new novel algorithm, called hCount, which can handle both insertion and deletion of items with a much less memory space than the best reported algorithm. Our algorithm is also superior in terms of precision, recall and processing time. In addition, our approach does not request the preknowledge on the size of range for a data stream, and can handle range extension dynamically. Given a little modification, algorithm hCount can be improved to hCount*, which even owns significantly better performance than before. 1

Improving End-to-End Sequential Recommendations with Intent-aware Diversification

Sequential recommenders that capture users' dynamic intents by modeling sequential behavior, are able to accurately recommend items to users. Previous studies on sequential recommendations (SRs) mostly focus on optimizing the recommendation accuracy, thus ignoring the diversity of recommended items. Many existing methods for improving the diversity of recommended items are not applicable to SRs because they assume that user intents are static and rely on post-processing the list of recommended items to promote diversity. We consider both accuracy and diversity by reformulating SRs as a list generation task, and propose an integrated approach with an end-to-end neural model, called intent-aware diversified sequential recommendation (IDSR). Specifically, we introduce an implicit intent mining (IIM) module for SR to capture multiple user intents reflected in sequences of user behavior. We design an intent-aware diversity promoting (IDP) loss function to supervise the learning of the IIM module and guide the model to take diversity into account during training. Extensive experiments on four datasets show that IDSR significantly outperforms state-of-the-art methods in terms of recommendation diversity while yielding comparable or superior recommendation accuracy