Graph vertices are often associated with attributes. For example, in addition to their connection relations, people in friendship networks have personal attributes, such as interests, age, and residence. Such graphs (networks) are called attributed graphs. The detection of clusters in attributed graphs is of great practical relevance, e.g., targeting ads. Attributes and edges often provide complementary information. The effective use of both types of information promises meaningful results. In this work, we propose a method called UNCut (for Unimodal Normalized Cut) to detect cohesive clusters in attributed graphs. A cohesive cluster is a subgraph that has densely connected edges and has as many homogeneous (unimodal) attributes as possible. We adopt the normalized cut to assess the density of edges in a graph cluster. To evaluate the unimodality of attributes, we propose a measure called unimodality compactness which exploits Hartigans' dip test. Our method UNCut integrates the normalized cut and unimodality compactness in one framework such that the detected clusters have low normalized cut and unimodality compactness values. Extensive experiments on various synthetic and real-world data verify the effectiveness and efficiency of our method UNCut compared with state-of-the-art approaches. Code and data related to this chapter are available at: https://www.dropbox.com/sh/xz2ndx65jai6num/AAC9RJ5PqQoYoxreItW83PrLa?dl=0 .

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