Mining weighted subgraphs in a single large graph

Le, Ngoc-Thao; Vo, Bay; Nguyen, Lam B. Q.; Fujita, Hamido; Le, Bac

doi:10.1016/j.ins.2019.12.010

Cited by 43 publications

(38 citation statements)

References 29 publications

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“…CECI [24] utilized the BFS-based filtering and reverse-BFS-based refinement to prune the unpromising candidates and then replaced the edge verification with set intersection to speed up the candidate verification. In addition, similar algorithms include the literature [25][26][27][28].…”

Section: G (A)mentioning

confidence: 99%

Supergraph Topology Feature Index for Personalized Interesting Subgraph Query in Large Labeled Graphs

Shan

Jia

et al. 2021

Complexity

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Interesting subgraph query aims to find subgraphs that are isomorphic to the given query graph from a data graph and rank the subgraphs according to their interestingness scores. However, the existing subgraph query approaches are inefficient when dealing with large-scale labeled data graph. This is caused by the following problems: (i) the existing work mainly focuses on unweighted query graphs, while ignoring the impact of query constraints on query results. (ii) Excessive number of subgraph candidates or complex joins between nodes in the subgraph candidates reduce the query efficiency. To solve these problems, this paper proposes an intelligent solution. Firstly, an Isotype Structure Graph Compression (ISGC) strategy is proposed to compress similar nodes in a graph to reduce the size of the graph and avoid unnecessary matching. Then, an auxiliary data structure Supergraph Topology Feature Index (STFIndex) is designed to replace the storage of the original data graph and improve the efficiency of an online query. After that, a partition method based on Edge Label Step Value (ELSV) is proposed to partition the index logically. In addition, a novel Top-K interest subgraph query approach is proposed, which consists of the multidimensional filtering (MDF) strategy, upper bound value (UBV) (Size-c) matching, and the optimizational join (QJ) method to filter out as many false subgraph candidates as possible to achieve fast joins. We conduct experiments on real and synthetic datasets. Experimental results show that the average performance of our approach is 1.35 higher than that of the state-of-the-art approaches when the query graph is unweighted, and the average performance of our approach is 2.88 higher than that of the state-of-the-art approaches when the query graph is weighted.

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Section: G (A)mentioning

confidence: 99%

Supergraph Topology Feature Index for Personalized Interesting Subgraph Query in Large Labeled Graphs

Shan

Jia

et al. 2021

Complexity

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“…The design first calculated the weights of the pruned subgraphs, followed by applying search space analytics for subgraph pruning. The subgraph mining approach, based on the weighted threshold, had effectively addressed the issues concerning storage space and processing time (Le et al, 2020). Consequently, the probabilistic approach was investigated for frequent mining patterns on the uncertain graphs.…”

Section: Related Workmentioning

confidence: 99%

An Improved Pattern Mining Technique for Graph Pattern Analysis Using a Novel Behavior of Artificial Bee Colony Algorithm

Sahu

2021

IJCAI

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Rising data complexity and volume in the network has attracted researchers towards substructure analysis. Subgraph mining is an area that has gained remarkable attention in the last couple of years to offer an intelligent analysis of more massive graphs and complicated data structures. It has been observed that graph pattern mining faces issues regarding the matching ruleset and complex instruction set execution problem. This paper introduces modern-day intelligence architecture based on Swarm Intelligence that is cross-validated by supervised machine learning mechanisms. A new behavior incorporated with a new inter and intra hive behavior is incorporated in Swarm based Artificial Bee Colony. The proposed work model is evaluated over two different datasets with more than 4900 nodes in the graph. The proposed framework is evaluated using True Detection Rate, False Detection Rate, precision, and F-Measure, demonstrating an average improvement of 9.8%, 8.35%, 8.35% and 9.15% against existing GraMi work that represent an enhanced performance of the proposed pattern mining technique.Povzetek: Uporabljene so raznovrstne metode umetne inteligence in strojnega učenja za iskanje vzorcev, tj. podgrafov v grafu.

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“…The results showed that FP-Growth outperforms Apriori for mining weighted patterns. Le et al [28] proposed a frequent subgraph algorithm on a weighted large graph. A novel strategy is developed which aims to compute the weight of all candidate subgraphs.…”

Section: Related Workmentioning

confidence: 99%

A general-purpose distributed pattern mining system

et al. 2020

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This paper explores five pattern mining problems and proposes a new distributed framework called DT-DPM: Decomposition Transaction for Distributed Pattern Mining. DT-DPM addresses the limitations of the existing pattern mining problems by reducing the enumeration search space. Thus, it derives the relevant patterns by studying the different correlation among the transactions. It first decomposes the set of transactions into several clusters of different sizes, and then explores heterogeneous architectures, including MapReduce, single CPU, and multi CPU, based on the densities of each subset of transactions. To evaluate the DT-DPM framework, extensive experiments were carried out by solving five pattern mining problems (FIM: Frequent Itemset Mining, WIM: Weighted Itemset Mining, UIM: Uncertain Itemset Mining, HUIM: High Utility Itemset Mining, and SPM: Sequential Pattern Mining). Experimental results reveal that by using DT-DPM, the scalability of the pattern mining algorithms was improved on large databases. Results also reveal that DT-DPM outperforms the baseline parallel pattern mining algorithms on big databases.

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Mining weighted subgraphs in a single large graph

Cited by 43 publications

References 29 publications

Supergraph Topology Feature Index for Personalized Interesting Subgraph Query in Large Labeled Graphs

Supergraph Topology Feature Index for Personalized Interesting Subgraph Query in Large Labeled Graphs

An Improved Pattern Mining Technique for Graph Pattern Analysis Using a Novel Behavior of Artificial Bee Colony Algorithm

A general-purpose distributed pattern mining system

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