Neighborhood based fast graph search in large networks

Khan, Arijit; Li, Nan; Yan, Xifeng; Guan, Ziyu; Chakraborty, Supriyo; Shu, Tao

doi:10.1145/1989323.1989418

Cited by 110 publications

(81 citation statements)

References 36 publications

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“…Khan et al [16] present 'Ness', a neighborhood based similarity search designed for graph datasets with a low incidence of automorphisms and a high incidence of noise (such as online social networks). Their method is based on an information propagation model which transforms a large network into a set of multidimensional vectors, which are then processed by indexing and similarity search algorithms.…”

Section: Recent Workmentioning

confidence: 99%

A Sub-graph Matching Method based on Calibration of Characteristics of Topological Footprint

Nettleton¹,

Dries²

2015

IJCA

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Approximate sub-graph matching is important in many graph data mining fields. At present, current solutions can be difficult to implement, have an expensive pre-processing phase, or only work for given types of graph. In this paper a novel generic approach is presented which addresses these issues. An approximate sub-graph matcher (A-SGM) calculates the distance between the topological characteristics (footprint) of the sub-graphs to be matched, applying a weighting to the different sub-graph characteristics and those of neighbor nodes. The weights are calibrated for each dataset with a simulated annealing process using sample sets of graph nodes to reduce computational cost, and an exact isomorphism matcher as a fitness function which takes into account how well the match maintains the neighboring node degree distributions. Benchmarking is performed on several state of the art methods and real and synthetic graph datasets to evaluate the precision, recall and computational cost. The results show that the A-SGM is competitive with state of the art methods in terms of precision, recall and execution time.

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Section: Recent Workmentioning

confidence: 99%

A Sub-graph Matching Method based on Calibration of Characteristics of Topological Footprint

Nettleton¹,

Dries²

2015

IJCA

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“…Lee et al give detailed comparisons of several well-known exact subgraph matching algorithms [20]. Algorithms for inexact subgraph matching problem are heuristic-based [17,18,22,27,28,39]. For example, TALE [28] by Tian et al first locates mappings of a subset of "important" nodes and then expands them to the whole graph.…”

Section: Related Workmentioning

confidence: 99%

“…For a given query graph Q, we denote a list of candidate matchings of a node v ∈ Q by cand(v). The candidate matchings of v contain nodes in G which are "similar" to v. The node similarity can be measured by comparing the two corresponding nodes [28], or comparing an r-hop neighborhood (usually r ≤ 2) of them [17,18]. Then cand(v) is sorted in the descending order of this measure.…”

Section: Local Searchmentioning

confidence: 99%

Subgraph Search in Large Graphs with Result Diversification

Yu¹,

Yuan

2014

Proceedings of the 2014 SIAM International Conference on Data Mining

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The problem of subgraph search in large graphs has wide applications in both nature and social science. The subgraph search results are typically ordered based on graph similarity score. In this paper, we study the problem of ranking the subgraph search results based on diversification. We design two ranking measures based on both similarity and diversity, and formalize the problem as an optimization problem. We give two efficient algorithms, the greedy selection and the swapping selection with provable performance guarantee. We also propose a novel local search heuristic with at least 100 times speedup and a similar solution quality. We demonstrate the efficiency and effectiveness of our approaches via extensive experiments.

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“…Similarly, approximate matching frameworks consist in either strict label similarity [8], or structural similarity [9] which restricts the number of findable solutions. Moreover, to the best of our knowledge, approximate graph matching using the aggregated search paradigm is novel and has never been tackled.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, a different type of approximate structural matching works has been proposed in [18], [8]. These works are based on concept propagation [19] and spreading activation [20] instead of classical matching schemes (graph isomorphism and similarity metric).…”

Section: Introductionmentioning

confidence: 99%

LaSaS: an Aggregated Search based Graph Matching Approach

Echbarthi

Kheddouci

2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Graph querying is crucial to fully exploit the knowledge within the widely used graph datasets. However, graph datasets are usually noisy which makes the approximate graph matching tools favored to overcome restrictive query answering. In this paper, we introduce a new framework of approximate graph matching based on aggregated search called Label and Structure Similarity Aggregated Search (LaSaS). LaSaS enables effective and efficient graph querying without considering any fixed schema of the data graph by (i) using the aggregated search strategy to increase the number of answers, (ii) using a lightweight graph similarity metric that takes into account nodes label and graph structure similarity to enable finding approximate matches and also by (iii) using a simple graph weight update routine instead of computing the maximum common subgraph which reduces the overall computation cost. We evaluated our proposed approach over the real-life DBpedia graph and results show the effectiveness and stability of the approach on different parameter settings. Moreover, results also show that LaSaS yields more precise matches in a shorter amount of time when compared to state-of-the-art related approaches.

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Neighborhood based fast graph search in large networks

Cited by 110 publications

References 36 publications

A Sub-graph Matching Method based on Calibration of Characteristics of Topological Footprint

A Sub-graph Matching Method based on Calibration of Characteristics of Topological Footprint

Subgraph Search in Large Graphs with Result Diversification

LaSaS: an Aggregated Search based Graph Matching Approach

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