Heli Sun

SHRINK: a structural clustering algorithm for detecting hierarchical communities in networks

Community detection is an important task for mining the structure and function of complex networks. Generally, there are several different kinds of nodes in a network which are cluster nodes densely connected within communities, as well as some special nodes like hubs bridging multiple communities and outliers marginally connected with a community. In addition, it has been shown that there is a hierarchical structure in complex networks with communities embedded within other communities. Therefore, a good algorithm is desirable to be able to not only detect hierarchical communities, but also identify hubs and outliers. In this paper, we propose a parameter-free hierarchical network clustering algorithm SHRINK by combining the advantages of density-based clustering and modularity optimization methods. Based on the structural connectivity information, the proposed algorithm can effectively reveal the embedded hierarchical community structure with multiresolution in large-scale weighted undirected networks, and identify hubs and outliers as well. Moreover, it overcomes the sensitive threshold problem of density-based clustering algorithms and the resolution limit possessed by other modularity-based methods. To illustrate our methodology, we conduct experiments with both real-world and synthetic datasets for community detection, and compare with many other baseline methods. Experimental results demonstrate that SHRINK achieves the best performance with consistent improvements.

A novel ensemble method for classifying imbalanced data

The class imbalance problems have been reported to severely hinder classification performance of many standard learning algorithms, and have attracted a great deal of attention from researchers of different fields. Therefore, a number of methods, such as sampling methods, cost-sensitive learning methods, and bagging and boosting based ensemble methods, have been proposed to solve these problems. However, these conventional class imbalance handling methods might suffer from the loss of potentially useful information, unexpected mistakes or increasing the likelihood of overfitting because they may alter the original data distribution. Thus we propose a novel ensemble method, which firstly converts an imbalanced data set into multiple balanced ones and then builds a number of classifiers on these multiple data with a specific classification algorithm. Finally, the classification results of these classifiers for new data are combined by a specific ensemble rule. In the empirical study, different class imbalance data handling methods including three conventional sampling methods, one cost-sensitive learning method, six Bagging and Boosting based ensemble methods, our previous method EM1vs1 and two fuzzy-rule based classification methods were compared with our method. The experimental results on 46 imbalanced data sets show that our proposed method is usually superior to the conventional imbalance data handling methods when solving the highly imbalanced problems. HighlightsWe propose a novel ensemble method to handle imbalanced binary data.The method turns imbalanced data learning into multiple balanced data learning.Our method usually performs better than the conventional methods on imbalanced data.

Towards online multiresolution community detection in large-scale networks.

The investigation of community structure in networks has aroused great interest in multiple disciplines. One of the challenges is to find local communities from a starting vertex in a network without global information about the entire network. Many existing methods tend to be accurate depending on a priori assumptions of network properties and predefined parameters. In this paper, we introduce a new quality function of local community and present a fast local expansion algorithm for uncovering communities in large-scale networks. The proposed algorithm can detect multiresolution community from a source vertex or communities covering the whole network. Experimental results show that the proposed algorithm is efficient and well-behaved in both real-world and synthetic networks.

gSkeletonClu: Density-Based Network Clustering via Structure-Connected Tree Division or Agglomeration

Community detection is an important task for mining the structure and function of complex networks. Many pervious approaches are difficult to detect communities with arbitrary size and shape, and are unable to identify hubs and outliers. A recently proposed network clustering algorithm, SCAN, is effective and can overcome this difficulty. However, it depends on a sensitive parameter: minimum similarity threshold

Revealing Density-Based Clustering Structure from the Core-Connected Tree of a Network

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A feature subset selection algorithm automatic recommendation method

, but provides no automated way to find it. In this paper, we propose a novel density-based network clustering algorithm, called gSkeletonClu (graph-skeleton based clustering). By projecting a network to its Core-Connected Maximal Spanning Tree (CCMST), the network clustering problem is converted to finding core-connected components in the CCMST. We discover that all possible values of the parameter

IncOrder: incremental density-based community detection in dynamic networks

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Backward Path Growth for Efficient Mobile Sequential Recommendation

lie in the edge weights of the corresponding CCMST. By means of tree divisive or agglomerative clustering, our algorithm can find the optimal parameter

QoRank: A query-dependent ranking model using LSE-based weighted multiple hyperplanes aggregation for information retrieval

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LinkLPA: A Link‐based Label Propagation Algorithm for Overlapping Community Detection in Networks

and detect communities, hubs and outliers in large-scale undirected networks automatically without any user interaction. Extensive experiments on both real-world and synthetic networks demonstrate the superior performance of gSkeletonClu over the baseline methods.