Scaling fine-grained modularity clustering for massive graphs
arXiv preprint arXiv:1905.11275, 2019•arxiv.org
Modularity clustering is an essential tool to understand complicated graphs. However,
existing methods are not applicable to massive graphs due to two serious weaknesses.(1) It
is difficult to fully reproduce ground-truth clusters due to the resolution limit problem.(2) They
are computationally expensive because all nodes and edges must be computed iteratively.
This paper proposes gScarf, which outputs fine-grained clusters within a short running time.
To overcome the aforementioned weaknesses, gScarf dynamically prunes unnecessary …
existing methods are not applicable to massive graphs due to two serious weaknesses.(1) It
is difficult to fully reproduce ground-truth clusters due to the resolution limit problem.(2) They
are computationally expensive because all nodes and edges must be computed iteratively.
This paper proposes gScarf, which outputs fine-grained clusters within a short running time.
To overcome the aforementioned weaknesses, gScarf dynamically prunes unnecessary …
Modularity clustering is an essential tool to understand complicated graphs. However, existing methods are not applicable to massive graphs due to two serious weaknesses. (1) It is difficult to fully reproduce ground-truth clusters due to the resolution limit problem. (2) They are computationally expensive because all nodes and edges must be computed iteratively. This paper proposes gScarf, which outputs fine-grained clusters within a short running time. To overcome the aforementioned weaknesses, gScarf dynamically prunes unnecessary nodes and edges, ensuring that it captures fine-grained clusters. Experiments show that gScarf outperforms existing methods in terms of running time while finding clusters with high accuracy.
arxiv.org
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