scholarly journals MINC-NRL: An Information-Based Approach for Community Detection

Algorithms ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 20
Author(s):  
Yinan Chen ◽  
Chuanpeng Wang ◽  
Dong Li
Keyword(s):  
Community Detection ◽  
Structural Characteristics ◽  
Synthetic Data ◽  
Representation Learning ◽  
Search Space ◽  
Research Field ◽  
Data Sets ◽  
Detection Approach ◽  
Target Network ◽  
Overlapping Community

Complex networks usually consist of dense-connected cliques, which are defined as communities. A community structure is a reflection of the local characteristics existing in the network topology, this makes community detection become an important research field to reveal the internal structural characteristics of networks. In this article, an information-based community detection approach MINC-NRL is proposed, which can be applied to both overlapping and non-overlapping community detection. MINC-NRL introduces network representation learning (NRL) to represent the target network as vectors, then generates a community evolution process based on these vectors to reduce the search space, and finally, finds the best community partition in this process using mutual information between network and communities (MINC). Experiments on real-world and synthetic data sets verifies the effectiveness of the approach in community detection, both on non-overlapping and overlapping tasks.

scholarly journals An Overlapping Community Detection Approach in Ego-Splitting Networks Using Symmetric Nonnegative Matrix Factorization

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 869
Author(s):  
Mingqing Huang ◽  
Qingshan Jiang ◽  
Qiang Qu ◽  
Abdur Rasool
Keyword(s):  
Community Detection ◽  
Matrix Factorization ◽  
Large Scale ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Overlapping Community Detection ◽  
Detection Approach ◽  
Overlapping Community ◽  
Large Scale Networks ◽  
Symmetric Nonnegative Matrix Factorization

Overlapping clustering is a fundamental and widely studied subject that identifies all densely connected groups of vertices and separates them from other vertices in complex networks. However, most conventional algorithms extract modules directly from the whole large-scale graph using various heuristics, resulting in either high time consumption or low accuracy. To address this issue, we develop an overlapping community detection approach in Ego-Splitting networks using symmetric Nonnegative Matrix Factorization (ESNMF). It primarily divides the whole network into many sub-graphs under the premise of preserving the clustering property, then extracts the well-connected sub-sub-graph round each community seed as prior information to supplement symmetric adjacent matrix, and finally identifies precise communities via nonnegative matrix factorization in each sub-network. Experiments on both synthetic and real-world networks of publicly available datasets demonstrate that the proposed approach outperforms the state-of-the-art methods for community detection in large-scale networks.

GA2RM: A GA-Based Action Rule Mining Method

2021 ◽  
pp. 2150012
Author(s):  
Shervin Hashemi ◽  
Pirooz Shamsinejad
Keyword(s):  
State Of The Art ◽  
Synthetic Data ◽  
Search Space ◽  
Data Sets ◽  
Mining Method ◽  
Classification Rules ◽  
Classification Methods ◽  
Rule Mining ◽  
Current State ◽  
Traditional Classification

Action Mining is a subfield of Data Mining that tries to extract actions from traditional data sets. Action Rule is a type of rule that suggests some changes in its consequent part. Extracting action rules from data has been one of the research interests in recent years. Current state-of-the-art action rule mining methods like DEAR typically take classification rules as their input; Since traditional classification methods have been designed for prediction and not for manipulation, therefore extracting action rules directly from data can result in more valuable action rules. Here, we have proposed a method to generate action rules directly from data. To tackle the problem of huge search space of action rules, a Genetic Algorithm has been devised. Different metrics have been defined for investigating the effectiveness of our proposed method and a large number of experiments have been done on real and synthetic data sets. The results show that our method can find from 20% to 10 times more interesting (in case of support and confidence) action rules in comparison with its competitors.

scholarly journals DETECTING OPINION-BASED GROUPS AND POLARIZATION IN SURVEY-BASED ATTITUDE NETWORKS AND ESTIMATING QUESTION RELEVANCE

2021 ◽  
Vol 24 (02) ◽  
Author(s):  
ALEJANDRO DINKELBERG ◽  
DAVID JP O’SULLIVAN ◽  
MICHAEL QUAYLE ◽  
PÁDRAIG MACCARRON
Keyword(s):  
Social Identity ◽  
Community Detection ◽  
Empirical Data ◽  
Group Formation ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Preparation ◽  
Detection Algorithms ◽  
Data Application ◽  
Identity Based

Networks, representing attitudinal survey data, expose the structure of opinion-based groups. We make use of these network projections to identify the groups reliably through community detection algorithms and to examine social-identity-based groups. Our goal is to present a method for revealing polarization and opinion-based groups in attitudinal surveys. This method can be broken down into the following steps: data preparation, construction of similarity-based networks, algorithmic identification of opinion-based groups, and identification of important items for community structure. We assess the method’s performance and possible scope for applying it to empirical data and to a broad range of synthetic data sets. The empirical data application points out possible conclusions (i.e. social-identity polarization), whereas the synthetic data sets mark out the method’s boundaries. Next to an application example on political attitude survey, our results suggest that the method works for various surveys but is also moderated by the efficacy of the community detection algorithms. Concerning the identification of opinion-based groups, we provide a solid method to rank the item’s influence on group formation and as a group identifier. We discuss how this network approach for identifying polarization can classify non-overlapping opinion-based groups even in the absence of extreme opinions.

Mining Closed Sequential Patterns in Progressive Databases

2013 ◽  
Vol 12 (03) ◽  
pp. 1350024
Author(s):  
R. B. V. Subramanyam ◽  
A. Suresh Rao ◽  
Ramesh Karnati ◽  
Somaraju Suvvari ◽  
D. V. L. N. Somayajulu
Keyword(s):  
Pattern Mining ◽  
Synthetic Data ◽  
Window Size ◽  
Search Space ◽  
Sequential Patterns ◽  
Data Sets ◽  
Time Stamp ◽  
Algorithmic Approach ◽  
Dynamic Databases ◽  
Search Space Pruning

Previous studies of Mining Closed Sequential Patterns suggested several heuristics and proposed some computationally effective techniques. Like, Bidirectional Extension with closure checking schemas, Back scan search space pruning, and scan skip optimization used in BIDE (BI-Directional Extension) algorithm. Many researchers were inspired with the efficiency of BIDE, have tried to apply the technique implied by BIDE to various kinds of databases; we toofelt that it can be applied over progressive databases. Without tailoring BIDE, it cannot be applied to dynamic databases. The concept of progressive databases explores the nature of incremental databases by defining the parameters like, Period of Interest (POI), user defined minimum support. An algorithm PISA (Progressive mIning Sequential pAttern mining) was proposed by Huang et al. for finding all sequential patterns over progressive databases. The structure of PISA helps in space utilization by limiting the height of the tree, to the length of POI and this issue is also a motivation for further improvement in this work. In this paper, a tree structure LCT (Label, Customer-id, and Time stamp) is proposed, and an approach formining closed sequential patterns using closure checking schemas across the progressive databases concept. The significance of LCT structure is, confining its height to a maximum of two levels. The algorithmic approach describes that the window size can be increased by one unit of time. The complexity of the proposed algorithmic approach is also analysed. The approach is validated using synthetic data sets available in Internet and shows a better performance in comparison to the existing methods.

scholarly journals Autotuning of Exascale Applications With Anomalies Detection

2021 ◽  
Vol 4 ◽  
Author(s):  
Dragi Kimovski ◽  
Roland Mathá ◽  
Gabriel Iuhasz ◽  
Fabrizio Marozzo ◽  
Dana Petcu ◽  
...  
Keyword(s):  
Empirical Evaluation ◽  
Search Space ◽  
Distributed Applications ◽  
Research Field ◽  
Novel Approach ◽  
Detection Approach ◽  
Parameter Variations ◽  
Hardware Failures ◽  
Time And Energy ◽  
Application Execution

The execution of complex distributed applications in exascale systems faces many challenges, as it involves empirical evaluation of countless code variations and application runtime parameters over a heterogeneous set of resources. To mitigate these challenges, the research field of autotuning has gained momentum. The autotuning automates identifying the most desirable application implementation in terms of code variations and runtime parameters. However, the complexity and size of the exascale systems make the autotuning process very difficult, especially considering the number of parameter variations that have to be identified. Therefore, we introduce a novel approach for autotuning exascale applications based on a genetic multi-objective optimization algorithm integrated within the ASPIDE exascale computing framework. The approach considers multi-dimensional search space with support for pluggable objective functions, including execution time and energy requirements. Furthermore, the autotuner employs a machine learning-based event detection approach to detect events and anomalies during application execution, such as hardware failures or communication bottlenecks.

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