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Graph-based substructure pattern mining with edge-weight
To represent complex inter-relationships among entities, weighted graphs are more useful than their unweighted counterparts. In a transactional graph...
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Attribute prediction of spatio-temporal graph nodes based on weighted graph diffusion convolution network
Spatio-temporal graph data can be analyzed by effectively mining for realizing spatio-temporal graph data prediction. It is of great significance to...
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An effective keyword search co-occurrence multi-layer graph mining approach
A combination of tools and methods known as "graph mining" is used to evaluate real-world graphs, forecast the potential effects of a given graph’s...
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Graph partitioning and visualization in graph mining: a survey
Graph mining is a process of obtaining one or more sub-graphs and has been a very attractive research topic over the last two decades. It has found...
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UGMINE: utility-based graph mining
Frequent pattern mining extracts most frequent patterns from databases. These frequency-based frameworks have limitations in representing users’...
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Mining technology trends in scientific publications: a graph propagated neural topic modeling approach
The past decades have witnessed significant progress in scientific research, where new technologies emerge and traditional technologies constantly...
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A weighted-link graph neural network for lung cancer knowledge classification
Visualized knowledge representation can more effectively help the public gain knowledge about lung cancer prevention, diagnosis, treatment, and...
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An Efficient Graph Mining Approach Using Evidence Based Fuzzy Soft Set Method
The Dempster-Shafer theory of evidence is a method of fuzzy soft that is a very hot way to deal with uncertainty in the information technology field....
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Imbalanced instance selection based on Laplacian matrix decomposition with weighted k-nearest-neighbor graph
Data are an essential component for building machine learning models. Linearly separable high-quality data are conducive to building efficient...
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Mining Frequent Geo-Subgraphs in a Knowledge Graph
Frequent subgraph mining aims to find all subgraphs that occur frequently in a graph database or in a single large graph. It finds applications in...
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UaMC: user-augmented conversation recommendation via multi-modal graph learning and context mining
Conversation Recommender System (CRS) engage in multi-turn conversations with users and provide recommendations through responses. As user...
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ArgusDroid: detecting Android malware variants by mining permission-API knowledge graph
Malware family variants make minor and relevant changes of behaviors based on the original malware. To analyze and detect family variants, security...
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Community detection based on improved user interaction degree, weighted quasi-local path-based similarity and frequent pattern mining
Community detection is a significant research area in social networks. Most methods use network topology, but combining it with user interactions...
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Index-free triangle-based graph local clustering
Motif-based graph local clustering (MGLC) is a popular method for graph mining tasks due to its various applications. However, the traditional...
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Aspect Based Sentiment Analysis Using Long-Short Term Memory and Weighted N-Gram Graph-Cut
In the current domain, aspect-based sentiment analysis is a much-explored area in sentiment classification. In this paper, an optimization method,...
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Autonomous graph mining algorithm search with best performance trade-off
The pervasiveness of graphs today has raised the demand for algorithms to answer various questions: Which products would a user like to purchase...
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Auto-Weighted Graph Regularization and Residual Compensation for Multi-view Subspace Clustering
Multi-view clustering has attractive intensive attention and proved to be more effective than single-view clustering. The mining and effective...
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An efficient and scalable approach for mining subgraphs in a single large graph
In many recent applications, a graph is used to simulate many complex systems, such as social networks, traffic models or bioinformatics, and the...
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A novel approach to discover frequent weighted subgraphs using the average measure
Mining a weighted single large graph has recently attracted many researchers. The WeGraMi algorithm is considered the state-of-the-art among current...
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An Experimental Evaluation of Summarisation-Based Frequent Subgraph Mining for Subgraph Searching
The subgraph searching is a fundamental operation for the analysis and exploration of graphs. Nowadays, molecular databases are nearing close to one...
