Contingency-Aware Influence Maximization: A Reinforcement Learning Approach

Chen, Haipeng; Qiu, Wei; Ou, Han-Ching; An, Bo; Tambe, Milind

doi:10.48550/arxiv.2106.07039

Cited by 1 publication

(2 citation statements)

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“…This induces computational burden in the training pipeline and the method of predicting node quality is not systematic. Recently, [10] developed a DRL algorithm for realistic IM by considering node willingness to be seed node. However, it requires the adjacency matrix as an input, and therefore, unscaleable for large graphs.…”

Section: A Contributionsmentioning

confidence: 99%

“…To better generalize the trained policy across different graphs, graph embedding techniques, such as Structure to Vector (S2V) [5] and Graph Convolutional Networks (GCNs) [8] are integrated as part of the RL value functions to extract the graph structure information. Primarily proposed to solve relatively simple tasks such as the traveling salesman problem (TSP) and the maximum vertex cover (MVC), some of the recent works [9], [10] extend it to the IM problem. However, they possess following shortcomings: (1) Formulation: Existing works such as GCOMB, S2V-DQN and GCN-TREESEARCH addresses a trivial IM problem without explicitly accounting the intrinsic and influence activations.…”

Section: Introductionmentioning

confidence: 99%

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GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization

Munikoti¹,

Natarajan²,

Halappanavar³

2022

Preprint

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Influence maximization (IM) is a combinatorial problem of identifying a subset of nodes called the seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size. IM has numerous applications such as viral marketing, epidemic control, sensor placement and other network-related tasks. However, the uses are limited due to the computational complexity of current algorithms. Recently, learning heuristics for IM have been explored to ease the computational burden. However, there are serious limitations in current approaches such as: (1) IM formulations only consider influence via spread and ignore self activation; (2) scalability to large graphs; (3) generalizability across graph families; (4) low computational efficiency with a large running time to identify seed sets for every test network. In this work, we address each of these limitations through a unique approach that involves (1) formulating a generic IM problem as a Markov decision process that handles both intrinsic and influence activations;(2) employing double Q learning to estimate seed nodes; (3) ensuring scalability via sub-graph based representations; and (4) incorporating generalizability via meta-learning across graph families. Extensive experiments are carried out in various standard networks to validate performance of the proposed Graph Meta Reinforcement learning (GraMeR) framework. The results indicate that GraMeR is multiple orders faster and generic than conventional approaches.

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Section: A Contributionsmentioning

confidence: 99%