Community detection in dynamic networks via adaptive label propagation

Abstract

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation process, which generally affects only a small portion of the network. This makes it respond to network changes at low computational cost. The effectiveness of ALPA has been tested on both synthetic and real-world networks, which shows that it can successfully identify and track dynamic communities. Moreover, ALPA could detect communities with high quality and accuracy compared to other methods. Therefore, being low-complexity and parameter-free, ALPA is a scalable and promising solution for some real-world applications of community detection in dynamic networks.

Community detection based on similarities of communication behavior in IP networks

“…Yu et al (2012) extended the LPA to increase its convergence rate by applying the existing traffic identification method to assign label values to only some nodes in the network graph during the initialization process of the algorithm. Han et al (2017) proposed an adaptive LPA (ALPA) to further increase the processing speed. In recent years, Xu et al 2013employed a bipartite graph to describe the communication relationships between source hosts and destination hosts, and then applied one-mode projection to both the source host set and the destination host set.…”

“…The ALPA algorithm proposed by Han et al (2017) in 2017 is based on the label propagation algorithm and propagate labels only on the active nodes to reduce computational cost of community detection. It takes into account the historical information and updates its solution through a local label propagation process.…”

Community Detection in Dynamic Social Networks: A Multi-Agent Aya based on Electric Field

“…Recently, some approaches for the detection of dynamic communities have been proposed [16], [17], [18], [19], [20], [5], [21], [22], [23], [24]. One way to analyze communities in an evolving network is to consider the dynamic graph as a succession of independent captures of the graph, all of which are static graphs.…”

“…So the evolution of the network is no longer considered as a succession of snapshots, but as a succession of modifications on the network. The idea is to start with an initial partition and to update it according to the latest evolution of the network instead of finding a new partition [28], [29], [23], [24]. The detection of communities is therefore not done on the whole network, but only by minor and successive local modifications.…”

“…For example, Nguyen NP et al [30] proposed a modularity-based algorithm which greedily changes memberships of nodes by optimizing a local modularity function whenever a small modification occurs in the network. In a similar way, LabelRankT [21] and ALPA [23] adjust its detecting communities according to the network modifications through a stabilized label propagation process by taking advantage of what is already obtained in the previous snapshots.…”