Incremental Lossless Graph Summarization
Jihoon Ko, Yunbum Kook, Kijung Shin

TL;DR
This paper introduces MoSSo, an incremental algorithm for lossless graph summarization that efficiently updates summaries of dynamic graphs in real-time, enabling scalable and effective graph compression.
Contribution
MoSSo is the first incremental lossless graph summarization algorithm capable of handling fully dynamic graphs efficiently.
Findings
MoSSo processes each graph change in less than 0.1 ms.
It scales to graphs with hundreds of millions of edges.
Achieves compression ratios comparable to batch methods.
Abstract
Given a fully dynamic graph, represented as a stream of edge insertions and deletions, how can we obtain and incrementally update a lossless summary of its current snapshot? As large-scale graphs are prevalent, concisely representing them is inevitable for efficient storage and analysis. Lossless graph summarization is an effective graph-compression technique with many desirable properties. It aims to compactly represent the input graph as (a) a summary graph consisting of supernodes (i.e., sets of nodes) and superedges (i.e., edges between supernodes), which provide a rough description, and (b) edge corrections which fix errors induced by the rough description. While a number of batch algorithms, suited for static graphs, have been developed for rapid and compact graph summarization, they are highly inefficient in terms of time and space for dynamic graphs, which are common in…
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