2 research outputs found
Dynamic Structural Similarity on Graphs
One way of characterizing the topological and structural properties of
vertices and edges in a graph is by using structural similarity measures.
Measures like Cosine, Jaccard and Dice compute the similarities restricted to
the immediate neighborhood of the vertices, bypassing important structural
properties beyond the locality. Others measures, such as the generalized edge
clustering coefficient, go beyond the locality but with high computational
complexity, making them impractical in large-scale scenarios. In this paper we
propose a novel similarity measure that determines the structural similarity by
dynamically diffusing and capturing information beyond the locality. This new
similarity is modeled as an iterated function that can be solved by fixed point
iteration in super-linear time and memory complexity, so it is able to analyze
large-scale graphs. In order to show the advantages of the proposed similarity
in the community detection task, we replace the local structural similarity
used in the SCAN algorithm with the proposed similarity measure, improving the
quality of the detected community structure and also reducing the sensitivity
to the parameter of the SCAN algorithm.Comment: 8 pages, 7 figures, 1 table, Submitted for peer-review to the
conference ASONAM 201
High-Quality Disjoint and Overlapping Community Structure in Large-Scale Complex Networks
In this paper, we propose an improved version of an agglomerative
hierarchical clustering algorithm that performs disjoint community detection in
large-scale complex networks. The improved algorithm is achieved after
replacing the local structural similarity used in the original algorithm, with
the recently proposed Dynamic Structural Similarity. Additionally, the improved
algorithm is extended to detect fuzzy and crisp overlapping community
structure. The extended algorithm leverages the disjoint community structure
generated by itself and the dynamic structural similarity measures, to compute
a proposed membership probability function that defines the fuzzy communities.
Moreover, an experimental evaluation is performed on reference benchmark graphs
in order to compare the proposed algorithms with the state-of-the-art.Comment: 8 pages, 5 figures, 3 tables, sent to peer-review to the
International Symposium on Foundations and Applications of Big Data Analytics
FAB 201