Target Community Detection With User’s Preference and Attribute Subspace

Abstract

Community detection in the network has become an invaluable tool to explore and reveal the internal organization of nodes. In particular, the target community detection focuses on discovering the ''local'' links within and connecting to the target community related to user's preference, which refers to a limited number of nodes in the whole network. A few works in the literature discuss the target community detection. In this paper, we propose a target community detection with user's preference and attribute subspace. Our method utilizes not only network structure but also node attributes within a certain subspace to quantify both internal consistency and external separability, which is able to capture a user preferred target community. First, the similarity between nodes is calculated via both attributes and structures, and the center node set of the target community can be obtained by extending the sample node given by the user with its neighbors. Second, an attribute subspace calculation method with entropy weights is established based on the center node set, and the attribute subspace of the target community can thus be deduced. Finally, the target community quality, which is the combination of internal connectivity and external separability, is defined, based on which the target community with a user's preference can be detected. The experimental results on both synthetic network and real-world network datasets demonstrated the efficiency and effectiveness of the proposed algorithm.

A Personalized Navigation Route Recommendation Strategy Based on Differential Perceptron Tracking User’s Driving Preference

“…As mentioned above, most traditional path planning schemes improve the speed and precision of path planning algorithmically, while ignoring the efect of user preferences [26][27][28][29][30] on path selection. In scenarios that combine user preferences and path planning, user preferences are unchanged by default.…”

Incorporating User's Preference into Attributed Graph Clustering

“…For example, the univariate projection for the dip test may cause information-loss in some cases. TCU-SA (Target Community Detection with User's Preference and Attribute Subspace) [25] first computes the similarities between vertices and then expand the query vertex set with its neighbors. Based on the expanded set, TCU-SA deduces the attribute subspace using an entroy method.…”

A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social Networks

“…Table 6 presents a preliminary performance comparison of these algorithms in terms of detected communities and the corresponding modularity Q. For karate club network, ASOCCA obtains two unique connected component sets: set1 = { (24,25,26,27,15,21,23,33,32,31,16,28,29,34,19,30,9), (11,10,13,12,20,14,22,18,1,2,3,4,5,6,7,8,17)} set2 = { (24,10,25,26,27,15,21,23,33,32,31,16,28,29,34,19,…”