Browsing by Author "He, Jingrui"

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    Fairness-Aware Clique-Preserving Spectral Clustering of Temporal Graphs
    Fu, Dongqi; Zhou, Dawei; Maciejewski, Ross; Croitoru, Arie; Boyd, Marcus; He, Jingrui (ACM, 2023-04-30)
    With the widespread development of algorithmic fairness, there has been a surge of research interest that aims to generalize the fairness notions from the attributed data to the relational data (graphs). The vast majority of existing work considers the fairness measure in terms of the low-order connectivity patterns (e.g., edges), while overlooking the higher-order patterns (e.g., k-cliques) and the dynamic nature of real-world graphs. For example, preserving triangles from graph cuts during clustering is the key to detecting compact communities; however, if the clustering algorithm only pays attention to triangle-based compactness, then the returned communities lose the fairness guarantee for each group in the graph. Furthermore, in practice, when the graph (e.g., social networks) topology constantly changes over time, one natural question is how can we ensure the compactness and demographic parity at each timestamp efficiently. To address these problems, we start from the static setting and propose a spectral method that preserves clique connections and incorporates demographic fairness constraints in returned clusters at the same time. To make this static method fit for the dynamic setting, we propose two core techniques, Laplacian Update via Edge Filtering and Searching and Eigen-Pairs Update with Singularity Avoided. Finally, all proposed components are combined into an end-to-end clustering framework named F-SEGA, and we conduct extensive experiments to demonstrate the effectiveness, efficiency, and robustness of F-SEGA.
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    Rare Category Analysis for Complex Data: A Review
    Zhou, Dawei; He, Jingrui (ACM, 2023-10)
    Despite the sheer volume of data being collected, it is often the rare categories that are of the most important in many high impact domains, ranging from financial fraud detection in online transaction networks to emerging trend detection in social networks, from spam image detection in social media to rare disease diagnosis in the medical decision support system. This survey aims to provide a concise review of the state-of-the-art techniques on complex rare category analysis, where the majority classes have a smooth distribution while the minority classes exhibit the compactness property in the feature space or subspace. More specifically, we start with the introduction, problem definition, and unique challenges of complex rare category analysis, then present a comprehensive review of recent advances that are designed for this problem setting, from rare category exploration without any label information to the exposition step that characterizes rare examples with a compact representation, from representing rare patterns in a salient embedding space to interpreting the prediction results and providing relevant clues for the end-users' interpretation; finally, we discuss the potential problems and shed light on the future directions of complex rare category analysis.