11,350 research outputs found
Local Edge Betweenness based Label Propagation for Community Detection in Complex Networks
Nowadays, identification and detection community structures in complex
networks is an important factor in extracting useful information from networks.
Label propagation algorithm with near linear-time complexity is one of the most
popular methods for detecting community structures, yet its uncertainty and
randomness is a defective factor. Merging LPA with other community detection
metrics would improve its accuracy and reduce instability of LPA. Considering
this point, in this paper we tried to use edge betweenness centrality to
improve LPA performance. On the other hand, calculating edge betweenness
centrality is expensive, so as an alternative metric, we try to use local edge
betweenness and present LPA-LEB (Label Propagation Algorithm Local Edge
Betweenness). Experimental results on both real-world and benchmark networks
show that LPA-LEB possesses higher accuracy and stability than LPA when
detecting community structures in networks.Comment: 6 page
On the relationship between Gaussian stochastic blockmodels and label propagation algorithms
The problem of community detection receives great attention in recent years.
Many methods have been proposed to discover communities in networks. In this
paper, we propose a Gaussian stochastic blockmodel that uses Gaussian
distributions to fit weight of edges in networks for non-overlapping community
detection. The maximum likelihood estimation of this model has the same
objective function as general label propagation with node preference. The node
preference of a specific vertex turns out to be a value proportional to the
intra-community eigenvector centrality (the corresponding entry in principal
eigenvector of the adjacency matrix of the subgraph inside that vertex's
community) under maximum likelihood estimation. Additionally, the maximum
likelihood estimation of a constrained version of our model is highly related
to another extension of label propagation algorithm, namely, the label
propagation algorithm under constraint. Experiments show that the proposed
Gaussian stochastic blockmodel performs well on various benchmark networks.Comment: 22 pages, 17 figure
Node-Centric Detection of Overlapping Communities in Social Networks
We present NECTAR, a community detection algorithm that generalizes Louvain
method's local search heuristic for overlapping community structures. NECTAR
chooses dynamically which objective function to optimize based on the network
on which it is invoked. Our experimental evaluation on both synthetic benchmark
graphs and real-world networks, based on ground-truth communities, shows that
NECTAR provides excellent results as compared with state of the art community
detection algorithms
On Efficiently Detecting Overlapping Communities over Distributed Dynamic Graphs
Modern networks are of huge sizes as well as high dynamics, which challenges
the efficiency of community detection algorithms. In this paper, we study the
problem of overlapping community detection on distributed and dynamic graphs.
Given a distributed, undirected and unweighted graph, the goal is to detect
overlapping communities incrementally as the graph is dynamically changing. We
propose an efficient algorithm, called \textit{randomized Speaker-Listener
Label Propagation Algorithm} (rSLPA), based on the \textit{Speaker-Listener
Label Propagation Algorithm} (SLPA) by relaxing the probability distribution of
label propagation. Besides detecting high-quality communities, rSLPA can
incrementally update the detected communities after a batch of edge insertion
and deletion operations. To the best of our knowledge, rSLPA is the first
algorithm that can incrementally capture the same communities as those obtained
by applying the detection algorithm from the scratch on the updated graph.
Extensive experiments are conducted on both synthetic and real-world datasets,
and the results show that our algorithm can achieve high accuracy and
efficiency at the same time.Comment: A short version of this paper will be published as ICDE'2018 poste
Community detection with spiking neural networks for neuromorphic hardware
We present results related to the performance of an algorithm for community
detection which incorporates event-driven computation. We define a mapping
which takes a graph G to a system of spiking neurons. Using a fully connected
spiking neuron system, with both inhibitory and excitatory synaptic
connections, the firing patterns of neurons within the same community can be
distinguished from firing patterns of neurons in different communities. On a
random graph with 128 vertices and known community structure we show that by
using binary decoding and a Hamming-distance based metric, individual
communities can be identified from spike train similarities. Using bipolar
decoding and finite rate thresholding, we verify that inhibitory connections
prevent the spread of spiking patterns.Comment: Conference paper presented at ORNL Neuromorphic Workshop 2017, 7
pages, 6 figure
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