An interdisciplinary survey of network similarity methods
This work serves researchers in systems biology and pattern recognition by bridging interdisciplinary gaps, though it is incremental as it synthesizes existing knowledge rather than proposing new methods.
The paper addresses the lack of integrated surveys on network similarity methods across systems biology and pattern recognition by providing an interdisciplinary introduction, using a citation network of 5,793 vertices from over two hundred papers to quantitatively identify key objectives and methods.
Comparative graph and network analysis play an important role in both systems biology and pattern recognition, but existing surveys on the topic have historically ignored or underserved one or the other of these fields. We present an integrative introduction to the key objectives and methods of graph and network comparison in each field, with the intent of remaining accessible to relative novices in order to mitigate the barrier to interdisciplinary idea crossover. To guide our investigation, and to quantitatively justify our assertions about what the key objectives and methods of each field are, we have constructed a citation network containing 5,793 vertices from the full reference lists of over two hundred relevant papers, which we collected by searching Google Scholar for ten different network comparison-related search terms. We investigate its basic statistics and community structure, and frame our presentation around the papers found to have high importance according to five different standard centrality measures.