Core Concepts
The goal is to find a seed set of at most k nodes that maximizes the sum of interest values of the influenced nodes in a social network under the Linear Threshold Model and Independent Cascade Model.
Abstract
The paper proposes the Interest Maximization problem, which is a variant of the Influence Maximization problem in social networks. The key aspects are:
- The input includes a graph G(V, E) with an interest value η(u) associated with each node u, representing the level of interest the node has in the information being propagated.
- The objective is to find a seed set S of at most k nodes that maximizes the sum of interest values of the influenced (aware) nodes under the Linear Threshold Model (LTM) and Independent Cascade Model (ICM).
- The authors prove that the Interest Maximization problem is NP-Hard under the LTM and provide a linear programming formulation for it.
- Four heuristic algorithms are proposed: Level Based Greedy Heuristic (LBGH), Maximum Degree First Heuristic (MDFH), Profit Based Greedy Heuristic (PBGH), and Maximum Profit Based Greedy Heuristic (MPBGH).
- Extensive experiments are conducted on real-world benchmark datasets, and the results show that MPBGH outperforms the other heuristics in maximizing the total interest value of the influenced nodes under both the LTM and ICM.
Stats
The paper does not provide any specific numerical data or statistics. The focus is on the problem definition, complexity analysis, algorithmic approaches, and experimental evaluation.
Quotes
There are no direct quotes from the paper that are particularly striking or support the key arguments.