Search Results

We consider the fractional influence maximization problem, i.e., identifying users on a social network to be incentivized with potentially partial discounts to maximize the influence on the network. The larger the discount given to a user, the higher the ...

Online social networks are crucial in propagating information and exerting influence through word-of-mouth transmission. Influence maximization (IM) is the fundamental task in social network analysis to find the group of nodes that maximizes the influence ...

Influence maximization (IM) is the fundamental study of social network analysis. The IM problem finds the top k nodes that have maximum influence in the network. Most of the studies in IM focus on maximizing the number of activated nodes in the static ...

We consider a ubiquitous scenario in the study of Influence Maximization (IM), in which there is limited knowledge about the topology of the diffusion network. We set the IM problem in a multi-round diffusion campaign, aiming to maximize the number of ...

The Influence Maximization (IM) problem seeks to discover the set of nodes in a graph that can spread the information propagation at most. This problem is known to be NP-hard, and it is usually studied by maximizing the influence (spread) and, optionally,...

Influence Maximization is an important problem in data science. Current solutions with theoretical guarantees are space-inefficient and have limited parallelism, limiting their scalability to large real-world graphs or on more processors. In this paper, ...

Link recommendation systems in online social networks (OSNs), such as Facebook's "People You May Know", Twitter's "Who to Follow", and Instagram's "Suggested Accounts", facilitate the formation of new connections among users. This paper addresses the ...

Due to the explosive rise of online social networks, social network analysis (SNA) has emerged as a significant academic field in recent years. Understanding and examining social relationships in networks through network analysis opens up numerous ...

A frequently studied problem in the context of digital marketing for online social networks is the influence maximization problem that seeks for an initial seed set of influencers to trigger an information propagation cascade (in terms of active message ...

Graph neural networks (GNNs) have achieved impressive performance in various graph-related tasks. However, recent studies have found that GNNs are vulnerable to adversarial attacks. Node injection attacks (NIA) become an emerging scenario of graph ...

Individuals may have a range of opinions on controversial topics. However, the ease of making friendships in online social networks tends to create groups of like-minded individuals, who propagate messages that reinforce existing opinions and ignore ...

Given a graph G, a cost associated with each node, and a budget B, the budgeted influence maximization (BIM) aims to find the optimal set S of seed nodes that maximizes the influence among all possible sets such that the total cost of nodes in S is no ...

Finding the seed set that maximizes the influence spread over a network is a well-known NP-hard problem. Though a greedy algorithm can provide near-optimal solutions, the subproblem of influence estimation renders the solutions inefficient. In this work, ...

Influence maximization (IM) aims to identify a small number of influential individuals to maximize the information spread and finds applications in various fields. It was first introduced in the context of viral marketing, where a company pays a few ...

The spread of contagions has been a central component of research on social networks. An abundance of literature shows that few nodes in the network contribute significantly to the final magnitude of the outbreak. The problem of finding the set of k ...

When designing interventions in public health, development, and education, decision makers rely on social network data to target a small number of people, capitalizing on peer effects and social contagion to bring about the most welfare benefits to the ...

Influence maximization (IM) aims to find a set of seed nodes in a social network that maximizes the influence spread. While most IM problems focus on classical influence cascades (e.g., Independent Cascade and Linear Threshold) which assume individual ...

We consider the influence maximization problem over a temporal graph, where there is a single fixed source. We deviate from the standard model of influence maximization, where the goal is to choose the set of most influential vertices. Instead, in our ...

We study online influence maximization (OIM) under a new model of decreasing cascade (DC). This model is a generalization of the independent cascade (IC) model by considering the common phenomenon of market saturation. In DC, the chance of an influence ...

The rapid growth in popularity of online social networks provides new opportunities in computer science, sociology, math, information studies, biology, business, and more. Social network analysis (SNA) is a paramount technique supporting understanding ...