Stochastic Sequential Decision Making over Expanding Networks with Graph Filtering
arXiv:2603.19501v1 Announce Type: new 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 g
arXiv:2603.19501v1 Announce Type: new 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 agents. Experiments on synthetic and real datasets from cold-start recommendation to COVID prediction highlight the benefits of using a sequential decision-making perspective over batch and online filtering alternatives.
Executive Summary
This article proposes a stochastic sequential decision-making framework for filtering networked data on expanding graphs. By modeling filter shifts as agents and using multi-agent reinforcement learning, the framework accounts for long-term rewards and expansion dynamics. A context-aware graph neural network is developed to parameterize the policy, tuning filter parameters based on graph and agent information. Experiments on synthetic and real datasets demonstrate the benefits of sequential decision-making over batch and online filtering alternatives. The proposed framework addresses limitations of existing methods, which rely on pre-designed filters or online learning and neglect future impacts.
Key Points
- ▸ Stochastic sequential decision-making framework for filtering networked data on expanding graphs
- ▸ Multi-agent reinforcement learning approach to account for long-term rewards and expansion dynamics
- ▸ Context-aware graph neural network for parameterizing the policy
- ▸ Experiments on synthetic and real datasets demonstrate improved performance
Merits
Strength in Addressing Uncertainty
The proposed framework effectively addresses the uncertainty inherent in expanding graphs, allowing for more informed decision-making.
Flexibility in Adaptation
The use of multi-agent reinforcement learning enables the framework to adapt to changing graph structures and node connections.
Demerits
Computational Complexity
The framework's reliance on multi-agent reinforcement learning may increase computational complexity, potentially limiting its scalability.
Data Requirements
The framework requires large datasets to effectively train the policy and context-aware graph neural network.
Expert Commentary
The article presents a novel and well-structured approach to stochastic sequential decision-making on expanding graphs. The use of multi-agent reinforcement learning and context-aware graph neural networks is a significant improvement over existing methods. However, the framework's scalability and data requirements may limit its practical applications. Further research is needed to address these concerns and fully realize the potential of this framework.
Recommendations
- ✓ Future research should focus on developing more efficient algorithms to reduce computational complexity and improve scalability.
- ✓ The framework should be applied to real-world scenarios to demonstrate its practical utility and inform policy decisions.
Sources
Original: arXiv - cs.LG
