JacSim: An accurate and efficient link-based similarity measure in graphs

Hamedani, Masoud Reyhani; Kim, Sang–Wook

doi:10.1016/j.ins.2017.06.005

Cited by 20 publications

(28 citation statements)

References 9 publications

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“…We note that M -score(a, b) is also calculated in the same way. We employed Jaccard to calculate these two scores since in the literature, it is a well-known similarity measure widely used to calculate the similarity of binary vectors (i.e., sets) in various topics such as image segmentation [10], document summarization [20], and similarity computation [14].…”

Section: Similarity Computationmentioning

confidence: 99%

SimAndro-plus: On computing similarity of android applications

Reyhani

Kim

2021

COMSIS J

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In this paper, we propose SimAndro-Plus as an improved variant of the state-of-the-art method, SimAndro, to compute the similarity of Android applications (apps) regarding their functionalities. SimAndro-Plus has two major differences with SimAndro: 1) it exploits two beneficial features to similarity computation, which are totally disregarded by SimAndro; 2) to compute the similarity score of an app-pair based on strings and package name features, SimAndro-Plus considers not only those terms co-appearing in both apps but also considers those terms appearing in one app while missing in the other one. The results of our extensive ex periments with three real-world datasets and a dataset constructed by human experts demonstrate that 1) each of the two aforementioned differences is really effective to achieve better accuracy and 2) SimAndro-Plus outperforms SimAndro in similarity computation by 14% in average.

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Section: Similarity Computationmentioning

confidence: 99%

SimAndro-plus: On computing similarity of android applications

Reyhani

Kim

2021

COMSIS J

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“…In [18], the authors propose an algorithm of Co-citation similarity, which is defined by the amount of all entities that cite both nodes together, but the algorithm only considers the direct neighbors of nodes, which cannot adapt the requirements of multi-step Top-k query. Reference [19] gives out an accurate iterative algorithm, which needs to calculate the SimRank similarity of all nodes and has a large calculation cost. Based on that, [20] puts forward an iterative algorithm to calculate the similarity of dimension reduction.…”

Section: Related Workmentioning

confidence: 99%

“…However, it needs much preparing work and when the graph updates, the similarity board need to renew, the algorithm has a low query efficiency on dynamic graph. In [19], a random walk algorithm based on SimRank is proposed, denote the similarity by the expectation of the time that two random walks use from two nodes to meet, so its efficiency is high, but it cannot guarantee the accuracy on dynamic graph. In [24] the authors give out a similarity query method on the heterogeneous information network, which uses x-star mode to realize similarity query, but it has a high cost and the efficiency on dynamic graph is low.…”

Section: Related Workmentioning

confidence: 99%

Node Similarity Top-k Query of the Large-Scale Dynamic Graph With Weak Repeated Path Constraint

Wang

Song

2019

IEEE Access

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Node similarity Top-k query has always been a hot topic in the graph field. The existing node similarity Top-k query methods often need to traverse the whole graph, so its iterative calculation process is very inefficient on the large-scale graph. Besides, when the graph changes dynamically, it is difficult to update the query process and results adaptively, leading to the low accuracy of the query results. To solve those problems, in this paper, we propose a node similarity Top-k query method of the large-scale dynamic graph with weak repeated path constraint. In this method, we first divide the similarity relation according to the correlation degree of nodes in the base graph. Then, we put forward the direct similarity and the generic similarity, meanwhile, give their definition and expression, prove the isolation of direct similarity query in the base graph based on the direct similarity relation. And then a quick query method for the direct similarity Top-k attached result set of i step walk is given on the basis of the value of Top-k. When the attached result set of the direct similarity query cannot satisfy the strict Top-k query requirement, by setting weak repeated path multilevel constraint and introducing the idea of the Monte Carlo simulation, we put forward single-edge weaken factor and change the base graph into many single-direction graphs by weak pageRank random walk mechanism, and then realize the cumulative calculation for the generic similarity Top-k complete result set in single-direction graph set. Finally, according to the dynamic characteristics of the graph, based on the principle of local adaptation, we set the trigger update strategy of the base graph and the linkage update strategy of the weak single-direction graph set to ensure the accuracy of the query and minimize the updating maintenance cost. The experiments show that the method in this paper has great advantages in query efficiency, query accuracy, and update efficiency. INDEX TERMS Division of similarity relation, isolation of the base graph, pageRank walk, weak repeated path constraint, adaptive updating. The associate editor coordinating the review of this manuscript and approving it for publication was Xiaogang Jin.

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“…Nowadays, graphs are becoming increasingly important since they are natural representations to encode relational structures in many domains (e.g., app's function-call diagrams, brain-region functional activities, bio-medical drug molecules, protein interaction networks, citation networks, and social networks), where nodes represent the domain's objects and links to their pairwise relationships [1][2][3][4][5][6][7]. Computing the similarity score between two nodes based on the graph structure is a fundamental task in a wide range of applications such as recommender systems, spam detection, graph clustering [8,9], web page ranking, citation analysis, social network analysis, k-nearest neighbor search [1,9], synonym expansion (i.e., search engine's query rewriting and text simplification), and lexicon extraction (i.e., automatically building bilingual lexicons from text corpora) [10].…”

Section: Introductionmentioning

confidence: 99%

“…Link-based similarity measures (in short, similarity measures) such as SimRank [11] are well-known and conventional techniques to compute the similarity of nodes only based on the graph structure. Recently, SimRank and its variants have attracted a growing interest in the areas of data mining and information retrieval [1,[8][9][10][12][13][14]. The philosophy of SimRank in similarity computation is that "two objects are similar if they are related to similar objects" [11].…”

Section: Introductionmentioning

confidence: 99%

On Investigating Both Effectiveness and Efficiency of Embedding Methods in Task of Similarity Computation of Nodes in Graphs

Hamedani

Kim

2020

Applied Sciences

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One of the important tasks in a graph is to compute the similarity between two nodes; link-based similarity measures (in short, similarity measures) are well-known and conventional techniques for this task that exploit the relations between nodes (i.e., links) in the graph. Graph embedding methods (in short, embedding methods) convert nodes in a graph into vectors in a low-dimensional space by preserving social relations among nodes in the original graph. Instead of applying a similarity measure to the graph to compute the similarity between nodes a and b, we can consider the proximity between corresponding vectors of a and b obtained by an embedding method as the similarity between a and b. Although embedding methods have been analyzed in a wide range of machine learning tasks such as link prediction and node classification, they are not investigated in terms of similarity computation of nodes. In this paper, we investigate both effectiveness and efficiency of embedding methods in the task of similarity computation of nodes by comparing them with those of similarity measures. To the best of our knowledge, this is the first work that examines the application of embedding methods in this special task. Based on the results of our extensive experiments with five well-known and publicly available datasets, we found the following observations for embedding methods: (1) with all datasets, they show less effectiveness than similarity measures except for one dataset, (2) they underperform similarity measures with all datasets in terms of efficiency except for one dataset, (3) they have more parameters than similarity measures, thereby leading to a time-consuming parameter tuning process, (4) increasing the number of dimensions does not necessarily improve their effectiveness in computing the similarity of nodes.

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JacSim: An accurate and efficient link-based similarity measure in graphs

Cited by 20 publications

References 9 publications

SimAndro-plus: On computing similarity of android applications

SimAndro-plus: On computing similarity of android applications

Node Similarity Top-k Query of the Large-Scale Dynamic Graph With Weak Repeated Path Constraint

On Investigating Both Effectiveness and Efficiency of Embedding Methods in Task of Similarity Computation of Nodes in Graphs

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