IM-META: Influence Maximization Using Node Metadata in Networks With Unknown Topology
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| Format: | Preprint |
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2021
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| _version_ | 1866916116581842944 |
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| author | Tran, Cong Shin, Won-Yong Spitz, Andreas |
| author_facet | Tran, Cong Shin, Won-Yong Spitz, Andreas |
| contents | Since the structure of complex networks is often unknown, we may identify the most influential seed nodes by exploring only a part of the underlying network, given a small budget for node queries. We propose IM-META, a solution to influence maximization (IM) in networks with unknown topology by retrieving information from queries and node metadata. Since using such metadata is not without risk due to the noisy nature of metadata and uncertainties in connectivity inference, we formulate a new IM problem that aims to find both seed nodes and queried nodes. In IM-META, we develop an effective method that iteratively performs three steps: 1) we learn the relationship between collected metadata and edges via a Siamese neural network, 2) we select a number of inferred confident edges to construct a reinforced graph, and 3) we identify the next node to query by maximizing the inferred influence spread using our topology-aware ranking strategy. Through experimental evaluation of IM-META on four real-world datasets, we demonstrate a) the speed of network exploration via node queries, b) the effectiveness of each module, c) the superiority over benchmark methods, d) the robustness to more difficult settings, e) the hyperparameter sensitivity, and f) the scalability. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2106_02926 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | IM-META: Influence Maximization Using Node Metadata in Networks With Unknown Topology Tran, Cong Shin, Won-Yong Spitz, Andreas Social and Information Networks Artificial Intelligence Information Theory Machine Learning Neural and Evolutionary Computing Since the structure of complex networks is often unknown, we may identify the most influential seed nodes by exploring only a part of the underlying network, given a small budget for node queries. We propose IM-META, a solution to influence maximization (IM) in networks with unknown topology by retrieving information from queries and node metadata. Since using such metadata is not without risk due to the noisy nature of metadata and uncertainties in connectivity inference, we formulate a new IM problem that aims to find both seed nodes and queried nodes. In IM-META, we develop an effective method that iteratively performs three steps: 1) we learn the relationship between collected metadata and edges via a Siamese neural network, 2) we select a number of inferred confident edges to construct a reinforced graph, and 3) we identify the next node to query by maximizing the inferred influence spread using our topology-aware ranking strategy. Through experimental evaluation of IM-META on four real-world datasets, we demonstrate a) the speed of network exploration via node queries, b) the effectiveness of each module, c) the superiority over benchmark methods, d) the robustness to more difficult settings, e) the hyperparameter sensitivity, and f) the scalability. |
| title | IM-META: Influence Maximization Using Node Metadata in Networks With Unknown Topology |
| topic | Social and Information Networks Artificial Intelligence Information Theory Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2106.02926 |