[2603.19501] Stochastic Sequential Decision Making over Expanding Networks with Graph Filtering

Computer Science > Machine Learning arXiv:2603.19501 (cs) [Submitted on 19 Mar 2026] Title:Stochastic Sequential Decision Making over Expanding Networks with Graph Filtering Authors:Zhan Gao, Bishwadeep Das, Elvin Isufi View a PDF of the paper titled Stochastic Sequential Decision Making over Expanding Networks with Graph Filtering, by Zhan Gao and Bishwadeep Das and Elvin Isufi View PDF HTML (experimental) Abstract:Graph filters leverage topological information to process networked data with existing methods mainly studying fixed graphs, ignoring that graphs often expand as nodes continually attach with an unknown pattern. The latter requires developing filter-based decision-making paradigms that take evolution and uncertainty into account. Existing approaches rely on either pre-designed filters or online learning, limited to a myopic view considering only past or present information. To account for future impacts, we propose a stochastic sequential decision-making framework for filtering networked data with a policy that adapts filtering to expanding graphs. By representing filter shifts as agents, we model the filter as a multi-agent system and train the policy following multi-agent reinforcement learning. This accounts for long-term rewards and captures expansion dynamics through sequential decision-making. Moreover, we develop a context-aware graph neural network to parameterize the policy, which tunes filter parameters based on information of both the graph and agent...