Detecting Social Positions Using Simulation

Brynielsson, Joel; Högberg, Johanna; Kaati, Lisa; Mårtenson, Christian; Svenson, Pontus

doi:10.1109/asonam.2010.52

Cited by 44 publications

(38 citation statements)

References 8 publications

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“…Some researchers [7], [8] even argue that graph simulation is more appropriate than subgraph isomorphism for modern problems like social network analysis because it yields matches that are conceptually more meaningful. With the rapid advent of Big Data, graphs have transformed into huge sizes and are rapidly getting out of the grasp of conventional computational approaches.…”

Section: Introductionmentioning

confidence: 99%

Techniques for Graph Analytics on Big Data

Nisar

Fard

Miller

2013

2013 IEEE International Congress on Big Data

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Abstract-Graphs enjoy profound importance because of their versatility and expressivity. They can be effectively used to represent social networks, web search engines and genome sequencing. The field of graph pattern matching has been of significant importance and has wide-spread applications. Conceptually, we want to find subgraphs that match a pattern in a given graph. Much work has been done in this field with solutions like Subgraph Isomorphism and Regular Expression matching. With Big Data, scientists are frequently running into massive graphs that have amplified the challenge that this area poses. We study the speedup and communication behavior of three distributed algorithms for inexact graph pattern matching. We also study the impact of different graph partitionings on runtime and network I/O. Our extensive results show that the algorithms exhibit excellent scalable behavior and min-cut partitioning can lead to improved performance under some circumstances, and can drastically reduce the network traffic as well.

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Section: Introductionmentioning

confidence: 99%

Techniques for Graph Analytics on Big Data

Nisar

Fard

Miller

2013

2013 IEEE International Congress on Big Data

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“…In contrast, no subgraph of G3 or G4 is isomorphic to P3, i.e., Miso(P3, Gi) is empty for i ∈ [3,4]. (2) P3 simG2 and P3 simG3.…”

Section: Examplementioning

confidence: 99%

“…The last experiments evaluated the efficiency of IncIsoMatch against VF2 and IsoUMatch, using synthetic data and 30 normal patterns generated with parameters (4,5,3,1). Fixing |V | = 15K, we varied |E| from 100K to 124K by inserting edges, in 4K increments.…”

Section: Exp-4: Incremental Subgraph Isomorphismmentioning

confidence: 99%

“…(1) For matching with graph simulation [1,3] we show the following. (a) The incremental matching problem is bounded for unit deletions and general graph patterns, and for unit insertions and dag patterns.…”

Section: Examplementioning

confidence: 99%

“…It is often defined in terms of subgraph isomorphism [26,28], graph simulation [1,3] or bounded simulation [8]. Given a pattern graph GP and a data graph G, graph pattern matching is to find the set M (GP , G) of matches in G for GP .…”

Section: Introductionmentioning

confidence: 99%

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Incremental graph pattern matching

Fan

Luo

et al. 2011

Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data

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Graph pattern matching has become a routine process in emerging applications such as social networks. In practice a data graph is typically large, and is frequently updated with small changes. It is often prohibitively expensive to recompute matches from scratch via batch algorithms when the graph is updated. With this comes the need for incremental algorithms that compute changes to the matches in response to updates, to minimize unnecessary recomputation. This paper investigates incremental algorithms for graph pattern matching defined in terms of graph simulation, bounded simulation and subgraph isomorphism. (1) For simulation, we provide incremental algorithms for unit updates and certain graph patterns. These algorithms are optimal: in linear time in the size of the changes in the input and output, which characterizes the cost that is inherent to the problem itself. For general patterns we show that the incremental matching problem is unbounded, i.e., its cost is not determined by the size of the changes alone. (2) For bounded simulation, we show that the problem is unbounded even for unit updates and path patterns. (3) For subgraph isomorphism, we show that the problem is intractable and unbounded for unit updates and path patterns. (4) For multiple updates, we develop an incremental algorithm for each of simulation, bounded simulation and subgraph isomorphism. We experimentally verify that these incremental algorithms significantly outperform their batch counterparts in response to small changes, using real-life data and synthetic data.

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Information Quality and Social Networks

Svenson¹

2019

Information Fusion and Data Science

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Detecting Social Positions Using Simulation

Cited by 44 publications

References 8 publications

Techniques for Graph Analytics on Big Data

Techniques for Graph Analytics on Big Data

Incremental graph pattern matching

Information Quality and Social Networks

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