ABEM: An Adaptive Agent-based Evolutionary Approach for Mining Influencers in Online Social Networks

• URL: http://arxiv.org/abs/2104.06563v1
• Date: Wed, 14 Apr 2021 00:31:08 GMT
• Title: ABEM: An Adaptive Agent-based Evolutionary Approach for Mining Influencers in Online Social Networks
• Authors: Weihua Li, Yuxuan Hu, Shiqing Wu, Quan Bai, Edmund Lai
• Abstract summary: A key step in evolutionary influence in online social networks is the identification of a small number of users, known as influencers. The evolving nature of the structure of these networks makes it difficult to locate and identify these influencers. We propose an adaptive agent-based approach to address this problem in the context of both static and dynamic networks.
• Score: 1.6128569396451058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A key step in influence maximization in online social networks is the identification of a small number of users, known as influencers, who are able to spread influence quickly and widely to other users. The evolving nature of the topological structure of these networks makes it difficult to locate and identify these influencers. In this paper, we propose an adaptive agent-based evolutionary approach to address this problem in the context of both static and dynamic networks. This approach is shown to be able to adapt the solution as the network evolves. It is also applicable to large-scale networks due to its distributed framework. Evaluation of our approach is performed by using both synthetic networks and real-world datasets. Experimental results demonstrate that the proposed approach outperforms state-of-the-art seeding algorithms in terms of maximizing influence.

Related papers

• Identification of important nodes in the information propagation network based on the artificial intelligence method [2.1331883629523634]
  We introduce a novel technique that combines the Decision-making Trial and Evaluation Laboratory (DEMATEL) and the Global Structure Model (GSM) This method is applied across various complex networks, such as social, transportation, and communication systems. Our analysis highlights the structural dynamics and resilience of these networks, revealing insights into node connectivity and community formation.
  arXiv  Detail & Related papers  (2024-02-29T23:43:08Z)
• Influencer Detection with Dynamic Graph Neural Networks [56.1837101824783]
  We investigate different dynamic Graph Neural Networks (GNNs) configurations for influencer detection. We show that using deep multi-head attention in GNN and encoding temporal attributes significantly improves performance.
  arXiv  Detail & Related papers  (2022-11-15T13:00:25Z)
• A Community-Aware Framework for Social Influence Maximization [40.842329580198324]
  We consider the Influence Maximization (IM) problem: 'if we can try to convince a subset of individuals in a social network to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target' We propose a community-aware divide-and-conquer strategy that involves (i) learning the inherent community structure of the social network, (ii) generating candidate solutions by solving the influence problem for each community, and (iii) selecting the final set of individuals from the candidate solutions using a novel progressive budgeting scheme.
  arXiv  Detail & Related papers  (2022-07-18T20:43:17Z)
• Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization [53.11458949694947]
  We consider an adaptive version of content-dependent online influence problem where seed nodes are sequentially activated based on realtime feedback. Our algorithm maintains a network model estimate and selects seed adaptively, exploring the social network while improving the optimal policy optimistically.
  arXiv  Detail & Related papers  (2022-06-29T18:17:28Z)
• Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction [59.221668173521884]
  We propose a novel framework to promote cascade size prediction by enhancing the user preference modeling. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate.
  arXiv  Detail & Related papers  (2022-04-18T09:25:06Z)
• Towards Modularity Optimization Using Reinforcement Learning to Community Detection in Dynamic Social Networks [0.0]
  We propose an approach to the problem of community detection in dynamic networks based on a reinforcement learning strategy. An experiment using synthetic and real-world dynamic network data shows results comparable to static scenarios.
  arXiv  Detail & Related papers  (2021-11-25T19:55:57Z)
• Offline Contextual Bandits for Wireless Network Optimization [107.24086150482843]
  In this paper, we investigate how to learn policies that can automatically adjust the configuration parameters of every cell in the network in response to the changes in the user demand. Our solution combines existent methods for offline learning and adapts them in a principled way to overcome crucial challenges arising in this context.
  arXiv  Detail & Related papers  (2021-11-11T11:31:20Z)
• CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks [14.695979686066062]
  We propose CLAIM - Curriculum LeArning Policy for Influence Maximization to improve the sample efficiency of RL methods. We conduct experiments on real-world datasets and show that our approach can outperform the current best approach.
  arXiv  Detail & Related papers  (2021-07-08T04:52:50Z)
• Network Diffusions via Neural Mean-Field Dynamics [52.091487866968286]
  We propose a novel learning framework for inference and estimation problems of diffusion on networks. Our framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities. Our approach is versatile and robust to variations of the underlying diffusion network models.
  arXiv  Detail & Related papers  (2020-06-16T18:45:20Z)
• Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks [17.295158818748188]
  We propose a scalable and flexible algorithm for user association based on multi-agent reinforcement learning. Users act as independent agents that, based on their local observations only, learn to autonomously coordinate their actions in order to optimize the network sum-rate. Simulation results show that the proposed algorithm is able to adapt to (fast) changes of radio environment, thus providing large sum-rate gain in comparison to state-of-the-art solutions.
  arXiv  Detail & Related papers  (2020-06-16T10:51:27Z)
• DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation [50.08581302050378]
  Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences. We propose a preliminary work of a neural influence diffusion network (i.e., DiffNet) for social recommendation (Diffnet) In this paper, we propose DiffNet++, an improved algorithm of Diffnet that models the neural influence diffusion and interest diffusion in a unified framework.
  arXiv  Detail & Related papers  (2020-01-15T08:45:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.