Influential Nodes in a Diffusion Model for Social Networks

By David Kempe, Jon M. Kleinberg, É. Tardos. International Colloquium on Automata, Languages and Programming, 2005

1,073 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/d7c5ace20b07669323611abfbd4c79bc0c9b9a03

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Duanbing Chen
2. Guojie Song
3. Xiaokui Xiao
4. Youze Tang
5. Yanchen Shi
6. Yajun Wang
7. Qingye Jiang
8. Xinran He

In Statistical and Nonlinear Physics

1. Duanbing Chen
2. Youze Tang
3. Yanchen Shi
4. Xiaokui Xiao
5. Tao Zhou
6. Linyuan Lu
7. Guojie Song
8. Kian-Lee Tan

Citations in the corpus

• Maximizing Social Influence in Nearly Optimal Time (2012, background): “Since its introduction, it has become one of the prominent models of influence spread; see for example [13, 12, 14, 27, 38, 24].”
• Dynamics of information diffusion and its applications on complex networks (2016, background): “A representative one is the decreasing cascade model [213].”
• Efficient influence maximization in social networks (2009, methodology): “Following the approach of [6] as well as later studies [7, 8, 9, 10, 1], we formulate the influence maximization problem as an optimization problem given a network structure and an influence cascade model, which are two key factors affecting the outcome of an influence propagation.”
• Fast algorithms for maximizing submodular functions (2014, background): “A second application arises in the area of social networks, where the functions characterizing the influence of a subset of nodes turn out to be submodular [17, 18].”
• Influence Maximization on Social Graphs: A Survey (2018, methodology): “For example, [51] extends the IC model to a Decreasing Cascade (DC) Model.”
• An event-based framework for characterizing the evolutionary behavior of interaction graphs (2007, background): “Kempe et al. [2003; Kempe et al. [2005] discuss a greedy algorithm for nding the initial set that maximizes the inuence.”
• Near-Optimal MAP Inference for Determinantal Point Processes (2012, background): “Informative subset selection problems arise in many applications where a small number of items must be chosen to represent or cover a much larger set; for instance, text summarization [1, 2], document and image search [3, 4, 5], sensor placement [6], viral marketing [7], and many others.”
• TwitterRank: finding topic-sensitive influential twitterers (2010, background): “WSDM 10, February 4 6, 2010, New York City, New York, USA.”
• Finding key players in complex networks through deep reinforcement learning (2020, background): “Changjun Fan 1,2 , Li Zeng 1 , Yizhou Sun 2 ✉ and Yang-Yu Liu 3,4 ✉ Finding an optimal set of nodes, called key players, whose activation (or removal) would maximally enhance (or degrade) a certain network functionality, is a fundamental class of problems in network science.”
• Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model (2011, methodology): “al [14, 15] propose a greedy algorithm to solve the influence maximization problem (brought about by Richardson [22]) to maximize the spreading of a single piece of ideas, innovations, etc.”
• A Survey of Statistical Network Models (2009, background): “The concept of information propagation also finds many applications in the network domain, such as virus propagation in computer networks [310], HIV infection networks [222; 163; 164], viral marketing [87] and more generally gossiping [170].”
• Influence maximization: near-optimal time complexity meets practical efficiency (2014, methodology): “Then, with a high probability, Greedy yields a (1 − 1/e − ε)-approximate solution under the IC model [18], where ε is a constant that depends on both G and r [4, 19].”
• Vital nodes identification in complex networks (2016, background): “Kempe, Kleinberg and Tardos [200] proposed a general framework for IMP under operational dynamical processes.”
• Competitive Influence Maximization in Social Networks (2007, background): “[3, 4] proved that the general version of influence maximization is NP-complete, and we conjecture it is so even for arborescences directed into a root.”