Abstract
Real-world graphs can be difficult to interpret and visualize beyond a certain size. To address this issue, graph summarization aims to simplify and shrink a graph, while maintaining its high-level structure and characteristics. Most summarization methods are designed for homogeneous, undirected, simple graphs; however, many real-world graphs are ornate; with characteristics including node labels, directed edges, edge multiplicities, and self-loops. In this paper we propose TG-sum, a versatile yet rigorous graph summarization model that (to the best of our knowledge, for the first time) can handle graphs with all the aforementioned characteristics (and any combination thereof). Moreover, our proposed model captures basic sub-structures that are prevalent in real-world graphs, such as cliques, stars, etc. Experiments demonstrate that TG-sum facilitates the visualization of real-world complex graphs, revealing interpretable structures and high-level relationships. Furthermore, TG-sum achieves better trade-off between compression rate and running time, relative to existing methods (only) on comparable settings.
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Notes
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\(L_\mathbb {N}(k)=2\log k +1\) bits are required to encode an arbitrarily large natural number k, using the variable-length prefix-free encoding; see, Ex. 2.4 in [12].
- 2.
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This work has been sponsored by the U.S. National Science Foundation CAREER 1452425 and the PwC Risk and Regulatory Services Innovation Center at Carnegie Mellon University. Any conclusions expressed in this material are those of the author and do not necessarily reflect the views, expressed or implied, of the funding parties.
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Berberidis, D., Liang, P.J., Akoglu, L. (2023). Summarizing Labeled Multi-graphs. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13714. Springer, Cham. https://doi.org/10.1007/978-3-031-26390-3_4
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