Targeted Influence Maximization in Social Networks

Song, Chonggang; Hsu, Wynne; Lee, Mong Li

doi:10.1145/2983323.2983724

Cited by 58 publications

(31 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As early in 1940s and 1990s, a group of outstanding scholars emerged to deeply analyze the reasons for the spread of personal and group rumors [15], [16], [19], [20]. [15] demonstrated that rumors will mutate during the process of communication and construct corresponding rumor formulas.…”

Section: Related Workmentioning

confidence: 99%

“…[22] proposed a distributed expression model of users combined with emotional factors to solve the problem of serious imbalances in positive and negative cases. [15], [19], [20] constructed the Independent Cascade Model with Login Event (IC-L) model to simulate the delay propagation process. [19] proposed a regression equation to explain the relationship between the distance between nodes in a social network and the probability of being infected.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Minimize Social Network Rumors Based on Rumor Path Tree

Wang

Fan

2020

IEEE Access

View full text Add to dashboard Cite

Social networks have become a powerful information spreading platform. How to limit rumor spread on social networks is a challenging problem. In this paper, we combine information spreading mechanisms to simulate real-world social network user behavior. Based on this, we estimate the risk degree of each node during the hazard period and analyze the hazard level that other nodes are potentially affected by when a node is infected by a rumor. We use the Rumor Path Tree (RP T) to analyze the rumor spreading path. By comparing the rumors and truths propagation to a certain node, the steps taken by the rumor node to propagation are estimated. In order to identify the truth node, we construct a fractional function to calculate the effective influence nodes, and select the node with the highest score from the generated RP T pool. Based on the truth node we effectively block the spread of rumors. Finally, experimental results and comparisons on the real datasets prove that our method is effective and efficient.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Minimize Social Network Rumors Based on Rumor Path Tree

Wang

Fan

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…On these models, they formulated IM problem as a combinatorial optimization problem and designed a natural greedy algorithm with approximation ratio is 1 − 1/e. IM has been received much attention from the following aspects: proposing efficiency algorithms [2][3][4][5][6][7][8][9]24] and studying its variants [7,10,[25][26][27][28].…”

Section: Influence Maximizationmentioning

confidence: 99%

Competitive Influence Maximization within Time and Budget Constraints in Online Social Networks: An Algorithmic Approach

Pham¹,

Duong²,

Hoang³

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Competitive Influence Maximization ( CIM ) problem, which seeks a seed set nodes of a player or a company to propagate their product’s information while at the same time their competitors are conducting similar strategies, has been paid much attention recently due to its application in viral marketing. However, existing works neglect the fact that the limited budget and time constraints can play an important role in competitive influence strategy of each company. In addition, based on the the assumption that one of the competitors dominates in the competitive influence process, the majority of prior studies indicate that the competitive influence function (objective function) is monotone and submodular.This led to the fact that CIM can be approximated within a factor of 1 − 1 / e − ϵ by a Greedy algorithm combined with Monte Carlo simulation method. Unfortunately, in a more realistic scenario where there is fair competition among competitors, the objective function is no longer submodular. In this paper, we study a general case of CIM problem, named Budgeted Competitive Influence Maximization ( BCIM ) problem, which considers CIM with budget and time constraints under condition of fair competition. We found that the objective function is neither submodular nor suppermodular. Therefore, it cannot admit Greedy algorithm with approximation ratio of 1 − 1 / e . We propose Sandwich Approximation based on Polling-Based Approximation ( SPBA ), an approximation algorithm based on Sandwich framework and polling-based method. Our experiments on real social network datasets showed the effectiveness and scalability of our algorithm that outperformed other state-of-the-art methods. Specifically, our algorithm is scalable with million-scale networks in only 1.5 min.

show abstract

“…In each iteration, it creates a seed-set S cur in three phases: (a) dummy element creation, (b) construction of three candidate seed-sets (one using ASR, a second using ASR L and a third using ASR U ), and (c) selection of the best candidate seed-set and removal of dummy elements from it. The iterations stop when S cur is not better than the previously created seed-set S pr in terms of ASR (steps [21][22]. This guarantees that the algorithm terminates [9].…”

Section: Examplementioning

confidence: 99%

“…There are many works on targeted viral marketing (e.g., [11,13,20,22,25]). For example, [13] considered influence maximization when each target node has a constant profit, and [22] considered the impact of the location and login time of target nodes. Unlike ours, the works in [11,13,20,22,25] do not consider vulnerable nodes.…”

Section: Related Workmentioning

confidence: 99%