Memetic algorithm with simulated annealing strategy and tightness greedy optimization for community detection in networks

Mu, Caihong; Xie, Jin; Liu, Yong; Chen, Feng; Liu, Yi; Jiao, Licheng

doi:10.1016/j.asoc.2015.05.034

Cited by 63 publications

(19 citation statements)

References 40 publications

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“…This area just spotlights on four agent models that are ordinarily utilized in the IM issue, to be specific Independent Cascade (IC) demonstrate, Linear Threshold (LT) show, Triggering (TR) model, and Time Aware model. We likewise quickly examine regular non-dynamic dissemination models [22]. Powerful Node Tracking on Dynamic Social Network: An Interchange Greedy Approach: In this paper we tend to explore a unique downside, particularly appropriate Node seek after disadvantage, as A growth of Influence Maximization disadvantage to dynamic frameworks [23].…”

Section: Proposed Systemmentioning

confidence: 99%

Certified Node Frequency in Social Network Using Parallel Diffusion Methods

Gopi¹,

Narayana²,

Venkatesh³

et al. 2019

ISI

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Informal community examination is one of the key zones of research amid present day times. The informal community is developing with more clients and the ties between them step by step. It has two parts: separating the versatile interpersonal organization into a few networks by considering data dispersion and choosing networks to discover persuasive hubs by a dynamic programming. A dynamic engendering model considering each the overall quality and individual fascination of the talk is given upheld sensible situation for sure, inside and out totally not the same as existing issues with impact decrease. Specifically, every center is distributed a versatility time limit. If the piece time of each customer outperforms that edge, the utility of the framework will decrease. Our proposed technique can discover new networks dependent on the past structure of the system without recomposing them sans preparation. We present an overview of agent techniques managing these issues and propose a scientific categorization that outlines the best in class. The goal is to give a thorough investigation and guide of existing endeavors around data dissemination in informal communities. The proposed calculation utilizes parallel preparing motor to determine this postpone issue in the present situation. The calculation in parallel discovers the prevalent seed set in the system and extends it in parallel to discover the network. Test results on the engineered and true informal organizations show that our strategy is both viable and proficient in finding networks in unique interpersonal organizations.

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Section: Proposed Systemmentioning

confidence: 99%

Certified Node Frequency in Social Network Using Parallel Diffusion Methods

Gopi¹,

Narayana²,

Venkatesh³

et al. 2019

ISI

View full text Add to dashboard Cite

show abstract

“…In recent years, some attempts tried to show that community structures are one of the significant characteristics in the most complex networks such as social networks due to numerous trends of human being to forming groups or communities. Due to the significant applications of community detection, several community detection approaches have been presented in literature which can be classified into six categories: spectral and clustering methods [20], [21], [15], [22], hierarchical algorithms [23], modularity-based methods [24], [25], evolutionary modelbased methods [26], [27], local community detection methods, and feature-based assisted methods [11]. Along with that total sixteen articles (published in 2015 to 2017) presented in this survey are summarized in Table 1 that contains eight columns.…”

Section: Community Detection Over Snsmentioning

confidence: 99%

“…Cai-hong mu et al [24] present a graph based greedy optimized community detection approach that use memetic algorithm (ma) based on genetic algorithm to compute local structural information of networks to improve the diversity of the population but increase computational cost. Xu Zhou [25] proposed an optimized Biogeography based Community detection approach over dynamic network.…”

Section: Community Detection Over Snsmentioning

confidence: 99%

Review of Community Detection over Social Media:Graph Prospective

Jain¹,

Singh²

2019

ijacsa

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Community over the social media is the group of globally distributed end users having similar attitude towards a particular topic or product. Community detection algorithm is used to identify the social atoms that are more densely interconnected relatively to the rest over the social media platform. Recently researchers focused on group-based algorithm and member-based algorithm for community detection over social media. This paper presents comprehensive overview of community detection technique based on recent research and subsequently explores graphical prospective of social media mining and social theory (Balance theory, status theory, correlation theory) over community detection. Along with that this paper presents a comparative analysis of three different state of art community detection algorithm available on I-Graph package on python i.e. walk trap, edge betweenness and fast greedy over six different social media data set. That yield intersecting facts about the capabilities and deficiency of community analysis methods.

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“…SA telah digunakan untuk permasalahan optimasi kombinatorial. SA merupakan metode dengan pendekatan pencarian solusi yang random (Mu et al, 2015).…”

Section: Pendahuluanunclassified

Metode Simulated Annealing untuk Optimasi Penjadwalan Perkuliahan Perguruan Tinggi

Sari

Suseno

2016

J. Sistem Info. Bisnis

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Course scheduling an assignment of courses and lecturers in the available time slots involving certain restrictions. Simulated annealing is a heuristic method can be used as search method and provide acceptable solutions with good results. The research aims to make scheduling courses at the college using simulated annealing using five variables data that lecturer courses, the time slot is comprised of the day and the time period and class room. The research has two objective functions to be generated, the first is the assignment of a lecturer on courses that will be of teaching, second lecturers and their assignment course on the time slot and the room available. The objective function is calculated by taking into account the restrictions involved to produce the optimal solution. The validation is performed by testing to simulated annealing method with an varian average of 77.791% of the data variance can reach a solution with a standard deviation of 3.931509. In this research given the method of solution in the use of the remaining search space to be reused by the data that is unallocated.Keywords: Scheduling; Timetabling; Simulated annealing; Heuristic; Objective function; Constraint AbstrakPenjadwalan kuliah merupakan penugasan mata kuliah dan dosen pengampu pada slot waktu yang tersedia dengan melibatkan batasan tertentu. Simulated annealing merupakan salah satu metode heuristic yang dapat digunakan sebagai metode pencarian dan memberikan solusi yang dapat diterima dengan hasil yang baik. Penelitian ini bertujuan untuk membuat penjadwalan mata kuliah pada perguruan tinggi menggunakan metode simulated annealing dengan lima variabel data yaitu dosen, mata kuliah, slot waktu yang terdiri dari hari dan waktu periode dan variabel ruang. Penelitian ini memiliki dua fungsi tujuan yang akan dihasilkan, pertama adalah penugasan dosen pada mata kuliah yang akan diampu, kedua penugasan mata kulia beserta dosen pada slot waktu dan ruang yang tersedia. Fungsi tujuan dihitung dengan memperhatikan batasan yang terlibat untuk menghasilkan solusi yang optimal. Validasi dilakukan dengan uji coba terhadap metode simulated annealing dengan menghasilkan rata-rata varian sebesar 77,791% data dapat mencapai solusi dengan standar deviasi sebesar 3.931509. Pada penelitian ini diberikan metode solusi dalam penggunaan ruang pencarian yang tersisa untuk dapat digunakan kembali oleh data yang belum teralokasikan.

show abstract

Memetic algorithm with simulated annealing strategy and tightness greedy optimization for community detection in networks

Cited by 63 publications

References 40 publications

Certified Node Frequency in Social Network Using Parallel Diffusion Methods

Certified Node Frequency in Social Network Using Parallel Diffusion Methods

Review of Community Detection over Social Media:Graph Prospective

Metode Simulated Annealing untuk Optimasi Penjadwalan Perkuliahan Perguruan Tinggi

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