CAMAS: A cluster-aware multiagent system for attributed graph clustering

Bu, Zhan; Gao, Guangliang; Li, Hui-Jia; Cao, Jie

doi:10.1016/j.inffus.2017.01.002

Cited by 48 publications

(10 citation statements)

References 14 publications

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“…Even though for expert search the different classification algorithms, information retrieval, and graph clustering work were already done; 13,18,71–74 however, the aim of this study is to predict upcoming rising stars by means of transfer learning in the area of two or more co‐author networks. As far as our knowledge is concerned, no previous work has been done regarding the measuring of topic‐level and node‐level social influence on large‐scale networks classification based on transfer learning and predicting upcoming rising stars.…”

Section: Methodsmentioning

confidence: 99%

Author classification using transfer learning and predicting stars in co‐author networks

et al. 2020

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Summary The vast amount of data is key challenge to mine a new scholar that is plausible to be star in the upcoming period. The enormous amount of unstructured data raise every year is infeasible for traditional learning; consequently, we need a high quality of preprocessing technique to expand the performance of traditional learning. We have persuaded a novel approach, Authors classification algorithm using Transfer Learning (ACTL) to learn new task on target area to mine the external knowledge from the source domain. Comprehensive experimental outcomes on real‐world networks showed that ACTL, Node‐based Influence Predicting Stars, Corresponding Authors Mutual Influence based on Predicting Stars, and Specific Topic Domain‐based Predicting Stars enhanced the node classification accuracy as well as predicting rising stars to compared with contemporary baseline methods.

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Section: Methodsmentioning

confidence: 99%

Author classification using transfer learning and predicting stars in co‐author networks

et al. 2020

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“…A binary tree updating algorithm is proposed and the images are recommended based on the combined feature search. Bu et al (2017) have proposed an agent-oriented strategy for attributed graph clustering. The individual nodes of attributed graphs are considered as an agent to extract the clusters that are overlapping.…”

Section: Related Workmentioning

confidence: 99%

An intelligent inventive system for personalised webpage recommendation based on ontology semantics

Deepak

Ahmed²,

Skanda

2019

IJISTA

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Owing to the information diversity in the web and its dynamically changing contents, extraction of relevant information from the web is a huge challenge. With the World Wide Web transforming into a more organised semantic web, the incorporation of semantic techniques to retrieve relevant information is highly necessary. In this paper, a dynamic ontology alignment technique for recommending relevant webpages is proposed. The strategy focuses on knowledge tree construction by computing the semantic similarity between the query terms as well as the ontological entities. Furthermore, the semantic similarity is again computed between nodes of the constructed knowledge tree and URLs in the URL repository to recommend relevant webpages. The dynamic ontology alignment by computing their respective semantic similarity constitutes Ontology Semantics. Personalisation is achieved by prioritisation of webpages by content-based analysis of the users' web usage data. An overall accuracy of 87.73% is achieved by the proposed approach.

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“…T HE past two decades have witnessed a great deal of research attention devoted to multi-agent systems (MASs) [7], [8], [13], [16], [19], [31], [55], [57]. Task allocation in MASs aims at finding appropriate agents who can independently or cooperatively conduct specific tasks [2], [25].…”

Section: Introductionmentioning

confidence: 99%

Task Allocation on Layered Multiagent Systems: When Evolutionary Many-Objective Optimization Meets Deep Q-Learning

Wang

et al. 2021

IEEE Trans. Evol. Computat.

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This paper is concerned with the multi-task multiagent allocation problem via many-objective optimization for multi-agent systems (MASs). First, a novel layered MAS model is constructed to address the multi-task multi-agent allocation problem that includes both the original task simplification and the many-objective allocation. In the first layer of the model, the deep Q-learning method is introduced to simplify the prioritization of the original task set. In the second layer of the model, the modified shift-based density estimation (MSDE) method is put forward to improve the conventional Strength Pareto Evolutionary Algorithm 2 (SPEA2) in order to achieve many-objective optimization on task assignments. Then, an MSDE-SPEA2-based method is proposed to tackle the many-objective optimization problem with objectives including task allocation, makespan, agent satisfaction, resource utilization, task completion, and task waiting time. As compared with existing allocation methods, the developed method in this paper exhibits an outstanding feature that the task assignment and the task scheduling are carried out simultaneously. Finally, extensive experiments are conducted to 1) verify the validity of the proposed model and the effectiveness of two main algorithms; and 2) illustrate the optimal solution for task allocation and efficient strategy for task scheduling under different scenarios.

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CAMAS: A cluster-aware multiagent system for attributed graph clustering

Cited by 48 publications

References 14 publications

Author classification using transfer learning and predicting stars in co‐author networks

Author classification using transfer learning and predicting stars in co‐author networks

An intelligent inventive system for personalised webpage recommendation based on ontology semantics

Task Allocation on Layered Multiagent Systems: When Evolutionary Many-Objective Optimization Meets Deep Q-Learning

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