SSumM: Sparse Summarization of Massive Graphs

Lee, Kyuhan; Jo, Hyeonsoo; Ko, Jihoon; Lim, Sungsu; Shin, Kijung

doi:10.1145/3394486.3403057

Cited by 35 publications

(32 citation statements)

References 46 publications

(123 reference statements)

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“…We report the performance of the compressing technique introduced in Section III. As a baseline technique, we report also for SSuM, a state of the art method that employs node merging and edge sparsifying to generate a super-graph as output [41]. Table VIII compares the performance of compression methods in terms of size (number of nodes and edges) of the compressed graph and of quality in the matching task (MRR).…”

Section: Compression Resultsmentioning

confidence: 99%

Unsupervised Matching of Data and Text

Ahmadi¹,

Hansjorg²,

Papotti³

2021

Preprint

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Entity resolution is a widely studied problem with several proposals to match records across relations. Matching textual content is a widespread task in many applications, such as question answering and search. While recent methods achieve promising results for these two tasks, there is no clear solution for the more general problem of matching textual content and structured data. We introduce a framework that supports this new task in an unsupervised setting for any pair of corpora, being relational tables or text documents. Our method builds a fine-grained graph over the content of the corpora and derives word embeddings to represent the objects to match in a low dimensional space. The learned representation enables effective and efficient matching at different granularity, from relational tuples to text sentences and paragraphs. Our flexible framework can exploit pre-trained resources, but it does not depends on their existence and achieves better quality performance in matching content when the vocabulary is domain specific. We also introduce optimizations in the graph creation process with an "expand and compress" approach that first identifies new valid relationships across elements, to improve matching, and then prunes nodes and edges, to reduce the graph size. Experiments on real use cases and public datasets show that our framework produces embeddings that outperform word embeddings and fine-tuned language models both in results' quality and in execution times.

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Section: Compression Resultsmentioning

confidence: 99%

Unsupervised Matching of Data and Text

Ahmadi¹,

Hansjorg²,

Papotti³

2021

Preprint

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“…For graph simplification, a number of works focused on algorithmic developments [38], [39], [40] and visualization [41], [42], [43], [44], [45]. In particular, Suh et al [46] proposed a topology-based graph simplification tool, which enables contraction of edges whose weight is below a user-specified threshold.…”

Section: Related Workmentioning

confidence: 99%

Topological Simplifications of Hypergraphs

Zhou¹,

Rathore²,

Purvine³

et al. 2021

Preprint

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We study hypergraph visualization via its topological simplification. We explore both vertex simplification and hyperedge simplification of hypergraphs using tools from topological data analysis. In particular, we transform a hypergraph to its graph representations known as the line graph and clique expansion. A topological simplification of such a graph representation induces a simplification of the hypergraph. In simplifying a hypergraph, we allow vertices to be combined if they belong to almost the same set of hyperedges, and hyperedges to be merged if they share almost the same set of vertices. Our proposed approaches are general, mathematically justifiable, and they put vertex simplification and hyperedge simplification in a unifying framework.

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“…Note that these batch algorithms are not designed to address changes in the input graph and should be rerun from scratch to reflect such changes. Lossy variants of the graph summarization problem were also explored in [15,16,21,24,27].…”

Section: Related Workmentioning

confidence: 99%

Incremental Lossless Graph Summarization

Kook

Shin

2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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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 practice.In this work, we propose MoSSo, the first incremental algorithm for lossless summarization of fully dynamic graphs. In response to each change in the input graph, MoSSo updates the output representation by repeatedly moving nodes among supernodes. MoSSo decides nodes to be moved and their destinations carefully but rapidly based on several novel ideas. Through extensive experiments on 10 real graphs, we show MoSSo is (a) Fast and 'any time': processing each change in near-constant time (less than 0.1 millisecond), up to 7 orders of magnitude faster than running state-of-the-art batch methods, (b) Scalable: summarizing graphs with hundreds of millions of edges, requiring sub-linear memory during the process, and (c) Effective: achieving comparable compression ratios even to state-of-the-art batch methods.

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SSumM: Sparse Summarization of Massive Graphs

Cited by 35 publications

References 46 publications

Unsupervised Matching of Data and Text

Unsupervised Matching of Data and Text

Topological Simplifications of Hypergraphs

Incremental Lossless Graph Summarization

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