Graph summarization with bounded error

Navlakha, Saket; Rastogi, Rajeev; Shrivastava, Nisheeth

doi:10.1145/1376616.1376661

Cited by 287 publications

(361 citation statements)

References 27 publications

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“…, where d av is the average degree of the nodes [22]. The average degree in our datasets is low so average running time is low.…”

Section: Graph Summarizationmentioning

confidence: 97%

“…Popular techniques are, amongst others, graph clustering, community detection and finding cliques. Our work builds upon two complementary graph methods: graph summarization [22] and dense subgraphs [26]. To the best of our knowledge, we are the first to consider the synergy of these two approaches.…”

Section: Related Workmentioning

confidence: 99%

“…The summary must however include a compact representation that can be easily visualized and that can be used for making predictions. While there are many methods to summarize graphs, we focus on the graph summarization (GS) approach of [21,22]. Their graph summary is an aggregate graph comprised of a signature and corrections.…”

Section: Graph Summarizationmentioning

confidence: 99%

“…A simple cost model that gives equal weight to supernodes, superedges and corrections was used in [22] and was used to produce Figure 4.…”

Section: Graph Summarizationmentioning

confidence: 99%

See 3 more Smart Citations

Link Prediction for Annotation Graphs Using Graph Summarization

Thor

Anderson

Raschid

et al. 2011

The Semantic Web – ISWC 2011

Self Cite

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Abstract. Annotation graph datasets are a natural representation of scientific knowledge. They are common in the life sciences where genes or proteins are annotated with controlled vocabulary terms (CV terms) from ontologies. The W3C Linking Open Data (LOD) initiative and semantic Web technologies are playing a leading role in making such datasets widely available. Scientists can mine these datasets to discover patterns of annotation. While ontology alignment and integration across datasets has been explored in the context of the semantic Web, there is no current approach to mine such patterns in annotation graph datasets. In this paper, we propose a novel approach for link prediction; it is a preliminary task when discovering more complex patterns. Our prediction is based on a complementary methodology of graph summarization (GS) and dense subgraphs (DSG). GS can exploit and summarize knowledge captured within the ontologies and in the annotation patterns. DSG uses the ontology structure, in particular the distance between CV terms, to filter the graph, and to find promising subgraphs. We develop a scoring function based on multiple heuristics to rank the predictions. We perform an extensive evaluation on Arabidopsis thaliana genes.

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“…, where d av is the average degree of the nodes [22]. The average degree in our datasets is low so average running time is low.…”

Section: Graph Summarizationmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Section: Graph Summarizationmentioning

confidence: 99%

“…A simple cost model that gives equal weight to supernodes, superedges and corrections was used in [22] and was used to produce Figure 4.…”

Section: Graph Summarizationmentioning

confidence: 99%

See 2 more Smart Citations

Link Prediction for Annotation Graphs Using Graph Summarization

Thor

Anderson

Raschid

et al. 2011

The Semantic Web – ISWC 2011

Self Cite

View full text Add to dashboard Cite

show abstract

“…A summary of a tripartite annotation graph is also a graph. While there are many methods to summarize graphs, we focus on the graph summarization (GS) approach of [15]. Their graph summary is an aggregate graph comprised of a signature and corrections.…”

Section: Graph Summarizationmentioning

confidence: 99%

Finding Cross Genome Patterns in Annotation Graphs

Benik

Chang

Raschid

et al. 2012

Lecture Notes in Computer Science

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Abstract. Annotation graph datasets are a natural representation of scientific knowledge. They are common in the life sciences where concepts such as genes and proteins are annotated with controlled vocabulary terms from ontologies. Scientists are interested in analyzing or mining these annotations, in synergy with the literature, to discover patterns. Further, annotated datasets provide an avenue for scientists to explore shared annotations across genomes to support cross genome discovery. We present a tool, PAnG (Patterns in Annotation Graphs), that is based on a complementary methodology of graph summarization and dense subgraphs. The elements of a graph summary correspond to a pattern and its visualization can provide an explanation of the underlying knowledge. We present and analyze two distance metrics to identify related concepts in ontologies. We present preliminary results using groups of Arabidopsis and C. elegans genes to illustrate the potential benefits of cross genome pattern discovery.

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Sampling business process event logs using graph‐based ranking model

Liu

Pei

Cheng

et al. 2020

Concurrency and Computation

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Summary Modern information systems are continuously collecting and storing large volumes of business process event logs. The analysis of event logs can provide valuable insights for business process re‐engineering and enhancement. Process discovery, as one of the most challenging event log analysis techniques, aims to discover a business process model from an event log. Many process discovery approaches have been proposed in the past two decades, however, most of them suffer from efficiency problem when dealing with large‐scale event logs. Motivated by PageRank, we propose LogRank, a graph‐based ranking model, for event log sampling in this paper. The LogRank is capable of sampling a large‐scale event log to a smaller size that can be efficiently handled by existing discovery approaches. To support real‐life applications, we instantiate the LogRank model for two typical types of event logs, that is, simple event logs and lifecycle event logs. To quantify the quality of a sample log with respect to the original one, we introduce a general evaluation framework that can be instantiated for different quality metrics. The proposed sampling approach has been implemented in the open‐source process mining toolkit ProM. By experiments with both synthetic and real‐life event logs, we demonstrate that the proposed LogRank‐based sampling approach provides an effective means to improve process discovery efficiency as well as guaranteeing high quality of discovered models.

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Graph summarization with bounded error

Cited by 287 publications

References 27 publications

Link Prediction for Annotation Graphs Using Graph Summarization

Link Prediction for Annotation Graphs Using Graph Summarization

Finding Cross Genome Patterns in Annotation Graphs

Sampling business process event logs using graph‐based ranking model

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