scholarly journals Taxonomic Superimposed Tree and Graph Mining Algorithms: A Comprehensive Study

2017 ◽  
Vol 174 (7) ◽  
pp. 29-36
Author(s):  
Saed Khawaldeh ◽  
Usama Pervaiz ◽  
Yeman B. ◽  
Tajwar A. ◽  
Vu Hoang
Keyword(s):  
Graph Mining ◽  
Mining Algorithms ◽  
Comprehensive Study

scholarly journals On-the-Fly Output Compression for Join-Based Graph Mining Algorithms

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 64008-64022 ◽  
Author(s):  
Mostofa Kamal Rasel ◽  
Young-Koo Lee
Keyword(s):  
Graph Mining ◽  
Mining Algorithms

scholarly journals GraphMineSuite

2021 ◽  
Vol 14 (11) ◽  
pp. 1922-1935
Author(s):  
Maciej Besta ◽  
Zur Vonarburg-Shmaria ◽  
Yannick Schaffner ◽  
Leonardo Schwarz ◽  
Grzegorz Kwasniewski ◽  
...  
Keyword(s):  
Graph Mining ◽  
High Performance ◽  
Maximal Clique ◽  
Building Blocks ◽  
Extensive Literature ◽  
Performance Bounds ◽  
Graph Representations ◽  
Fine Grained ◽  
Performance Metric ◽  
Mining Algorithms

We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms. First, GMS comes with a benchmark specification based on extensive literature review, prescribing representative problems, algorithms, and datasets. Second, GMS offers a carefully designed software platform for seamless testing of different fine-grained elements of graph mining algorithms, such as graph representations or algorithm subroutines. The platform includes parallel implementations of more than 40 considered baselines, and it facilitates developing complex and fast mining algorithms. High modularity is possible by harnessing set algebra operations such as set intersection and difference, which enables breaking complex graph mining algorithms into simple building blocks that can be separately experimented with. GMS is supported with a broad concurrency analysis for portability in performance insights, and a novel performance metric to assess the throughput of graph mining algorithms, enabling more insightful evaluation. As use cases, we harness GMS to rapidly redesign and accelerate state-of-the-art baselines of core graph mining problems: degeneracy reordering (by >2X), maximal clique listing (by >9×), k -clique listing (by up to 1.1×), and subgraph isomorphism (by 2.5×), also obtaining better theoretical performance bounds.

Graphical Data Mining

2011 ◽  
pp. 950-956
Author(s):  
Carol J. Romanowski
Keyword(s):  
Data Mining ◽  
Graph Mining ◽  
Graph Matching ◽  
Learning Task ◽  
Data Types ◽  
Area Of Interest ◽  
Graphical Data ◽  
Minimum Number ◽  
Categorical Attributes ◽  
Mining Algorithms

Data mining has grown to include many more data types than the “traditional” flat files with numeric or categorical attributes. Images, text, video, and the internet are now areas of burgeoning data mining research. Graphical data is also an area of interest, since data in many domains—such as engineering design, network intrusion detection, fraud detection, criminology, document analysis, pharmacology, and biochemistry—can be represented in this form. Graph mining algorithms and methods are fewer and less mature than those designed for numerical or categorical data. In addition, the distinction between graph matching and graph mining is not always clear. In graph mining, we often want to find all possible frequent subgraphs of all possible sizes that occur a specified minimum number of times. That goal involves iteratively matching incrementally larger subgraphs, while classical graph matching is a single search for a static subgraph. Also, graph mining is an unsupervised learning task. Instead of searching for a single match to a specific graph, we are looking for known or unknown graphs embedded in the data.

On recommendation of graph mining algorithms for different data

2016 ◽  
Author(s):  
Chih-Hua Tai ◽  
Tsung-Han Lee ◽  
Sheng-Hao Chiang ◽  
Jui-Yi Tsai ◽  
De-Nian Yang ◽  
...  
Keyword(s):  
Graph Mining ◽  
Mining Algorithms

scholarly journals An Attention-Based Graph Neural Network for Spam Bot Detection in Social Networks

2020 ◽  
Vol 10 (22) ◽  
pp. 8160
Author(s):  
Chensu Zhao ◽  
Yang Xin ◽  
Xuefeng Li ◽  
Hongliang Zhu ◽  
Yixian Yang ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Graph Mining ◽  
Rapid Development ◽  
Complex Method ◽  
Detection Model ◽  
Security Problem ◽  
Bot Detection ◽  
Network Platform ◽  
Mining Algorithms

With the rapid development of social networks, spam bots and other anomaly accounts’ malicious behavior has become a critical information security problem threatening the social network platform. In order to reduce this threat, the existing research mainly uses feature-based detection or propagation-based detection, and it applies machine learning or graph mining algorithms to identify anomaly accounts in social networks. However, with the development of technology, spam bots are becoming more advanced, and identifying bots is still an open challenge. This paper proposes a new semi-supervised graph embedding model based on a graph attention network for spam bot detection in social networks. This approach constructs a detection model by aggregating features and neighbor relationships, and learns a complex method to integrate the different neighborhood relationships between nodes to operate the directed social graph. The new model can identify spam bots by capturing user features and two different relationships among users in social networks. We compare our method with other methods on real-world social network datasets, and the experimental results show that our proposed model achieves a significant and consistent improvement.

scholarly journals Evaluation of Proposed Algorithm with Preceding GMT for Fraudulence Diagnosis

2016 ◽  
Vol 9 (2) ◽  
pp. 57-65
Author(s):  
Justin Kurland ◽  
Peng Chen
Keyword(s):  
Graph Mining ◽  
Graph Database ◽  
Financial Organization ◽  
The Future ◽  
The Individual ◽  
Mining Algorithms ◽  
The Relationship ◽  
Authentic Activities

Formerly existing graph mining algorithms regularly accept that database is generally static. To defeat that we proposed another algorithm which manages extensive database including the components which catches the properties of the graph in a couple of parameters and check the relationship among them in both left and additionally right course, in this way embracing DFS and in addition BFS approach. It furthermore discovers the subgraph by traversing the graph and removing the planned routine. The proposed calculation is utilized for identification of wrongdoing as a part of BANK & Financial organization by catching the properties and distinguishing the relationship and affiliations that may exist between the individual required in that wrongdoing which keep a few violations that may happen in future. We have utilized the Neo-ECLIPSE for the execution of proposed calculation and Neo4j is the graph database utilized for evaluation. On the off chance that a man endeavoring to confer fraud or engage in some kind of illicit movement, they will endeavor to pass on their activities as near authentic activities as could reasonably be expected. Here in this paper, we are giving the data that a man who is in beginning the phase of the fraud, what co-related wrongdoings or illicit exercises he can do in future. The future exercises that can be performed by the individual can be ceased by demonstrating the associations with the entries saved in the database.

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