Influence Maximization

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10 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Optimizing Marketing Budgets for Social Influence: A Generalized Fractional Incentives Framework
Oct 19, 2026Abhishek K. Umrawal, Akhil Bhimaraju, Eliot W. Robson +1Influence Maximization
University of Illinois Urbana-Champaign, Urbana, IL 61801, USA · Narmi, New York, NY 10016, USA · Stony Brook University, Stony Brook, NY 11794 USA
Graph Neural Networks for Influence Maximization in Social Networks: An Unsupervised Minimum Dominating Set Approach
Sep 12, 2026Erfan Ahmadi, Mina Shirazi, Behnam BahrakSocial Network Analysis Influence Maximization
Computer Engineering Department, Tehran Institute for Advanced Studies (TeIAS), Tehran, Iran. · University of Tehran, Tehran, Iran.
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search
Aug 9, 2026Yiqiao Liao, Parinaz NaghizadehNeural Surrogate Modeling Influence Maximization
UC San Diego
Delayed Verification Destabilizes Multi-Agent LLM Belief: Instability Thresholds and Optimal Corrector Placement
Jun 25, 2026Igor ItkinAI Agent Reliability Multi-Agent Coordination
Independent Researcher, Tel Aviv, Israel
PostDeg: Placement Beats Parameterization in LayerNorm GNNs
Jun 12, 2026Yash Tomar, Aryav DasGraph Neural Networks Layer Normalization
Purdue University · Park Tudor High School
Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark
Jun 10, 2026Mateusz Stolarski, Michał Czuba, Piotr Bielak +1Graph Coarsening Graph Sparsification
Wrocław University of Science and Technology, Wrocław, Poland
Revealing graph bandits for maximizing local influence
May 1, 2026Alexandra Carpentier, Michal ValkoMulti-Armed Bandits Graph Bandits
Universität Potsdam · SequeL team, Inria Lille - Nord Europe
Budgeted Online Influence Maximization
Apr 21, 2026Pierre Perrault, Jennifer Healey, Zheng Wen +1Influence Maximization Social Network Analysis
Adobe Research, San Jose, CA · ENS Paris-Saclay · DeepMind +1
Enhancing Discrete Particle Swarm Optimization for Hypergraph-Modeled Influence Maximization
Apr 17, 2026Qianshi Wang, Xilong Qu, Wenbin Pei +2Metaheuristic Optimization Particle Swarm Optimization
International School of Information Science & Engineering, Dalian University of Technology, Dalian 116024, China · School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China; Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education, Dalian 116024, China · School of Artificial Intelligence, Shanxi University, Taiyuan 237016, China +1
SP-GCRL: Influence Maximization on Incomplete Social Graphs
Mar 31, 2026Haohua Niu, Yuxuan Yang, Lingfeng Zhang +4Influence Maximization Social Network Analysis
School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou, China · School of Finance, Jiangxi University of Finance and Economics, Jiangxi, China · Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, China +1

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Optimizing Marketing Budgets for Social Influence: A Generalized Fractional Incentives Framework

Abhishek K. Umrawal, Akhil Bhimaraju, Eliot W. Robson, Lav R. Varshney

University of Illinois Urbana-Champaign, Urbana, IL 61801, USA · Narmi, New York, NY 10016, USA · Stony Brook University, Stony Brook, NY 11794 USA

Influence Maximization

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Marketing managers routinely allocate limited budgets across individuals in social networks whose responsiveness and influence vary substantially. We study the problem of Budgeted Generalized Fractional Influence Maximization (BGFIM), which captures the allocation of continuous levels of marketing spend across users in the presence of heterogeneous response and network spillovers. In this framework, adoption probabilities are governed by user-specific response functions that capture both baseline propensity and marginal responsiveness, while incentive costs vary across users. We propose a scalable solution approach based on a discrete greedy procedure with provable approximation guarantees, enabling tractable optimization under realistic budget constraints. Our formulation generalizes prior influence maximization models by allowing heterogeneous response and cost structures, providing a more flexible representation of targeted marketing interventions. Using experiments on multiple large-scale networks, we show that continuous allocation strategies can outperform standard heuristics and improve influence outcomes under heterogeneous conditions, and we illustrate how the framework can support profit-oriented decision making by identifying budget levels that balance marginal returns and costs. Overall, our results highlight the value of fine-grained budget allocation in networked marketing environments and offer a scalable approach for data-driven marketing resource allocation.