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The conventional algorithm (COPRA -- Community Overlap PRopagation Algorithm) proposed by Steve Gregory is efficient and useful in Complex Networks, but it is a challenge to select a suitable parameter "thr" as the input of the algorithm. In this paper, we put forward a threshold based label propagation algorithm, in which each vertex in the network is identified with a threshold respectively,...
In this paper, we develop an algorithm to cluster places not only based on their locations but also their semantics. Specifically, two places are considered similar if they are spatially close and visited by people of similar communities. With the explosion in the availability of location-tracking technologies, it has become easy to track locations and movements of users through user "check-ins"...
This article presents an efficient hierarchical clustering algorithm that solves the problem of core community detection. It is a variant of the standard community detection problem in which we are particularly interested in the connected core of communities. To provide a solution to this problem, we question standard definitions on communities and provide alternatives. We propose a function called...
The big data analytics community has accepted MapReduce as a programming model for processing massive data on distributed systems such as a Hadoop cluster. MapReduce has been evolving to improve its performance. We identified skewed workload among workers in the MapReduce ecosystem. The problem of skewed workload is of serious concern for massive data processing. We tackled the workload balancing...
This paper introduces a novel technique to track structures in time evolving graphs. The method is based on a parameter free approach for three-dimensional co-clustering of the source vertices, the target vertices and the time. All these features are simultaneously segmented in order to build time segments and clusters of vertices whose edge distributions are similar and evolve in the same way over...
In this study we analyze behavior of two types of coefficients for determining the suitable number of clusters obtained when fuzzy cluster analysis is applied. First one is Dunn's coefficient which contains membership degrees in its computational formula; second one is the average silhouette width, used primarily for evaluating hard clustering. There have already been attempts to compare different...
The size and interconnectedness of social networks continues to increase. As a result, finding communities or subsets of like nodes within these large networks has become a resource-intensive endeavor. In this paper, we characterize community-finding organized on the basis of network/set properties, and describe an agglomerative algorithm called egocentric community finding. The primary contribution...
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