Influence Maximization for Complementary Goods

Ou, Han-Ching; Chou, Chung‐Kuang; Chen, Ming-Syan

doi:10.1145/2983323.2983741

Cited by 11 publications

(6 citation statements)

References 13 publications

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“…To solve this problem, Li et al [18] employed game theory for solving the influence maximization in competitive networks. Ou et al [19] also investigated the issue of comparative influence maximization. They proposed interacting influence maximization game to solve the problem by broadening the existing model of the competitive influence maximization game.…”

Section: Related Workmentioning

confidence: 99%

“…To avoid activating the unwanted users in T , we select the node x with the largest value of (x) defined in (20), which depends on the value of δ V \T (x, S) − δ T (x, S). It can be seen from (19), that δ V \T (x, S) and δ T (x, S) are respectively the numbers of vertices outside and inside T that x can activate. Maximizing (x) in (20) is to choose the seed x such that the influence spreading of the new seed set S ∪ {x} in V \T can be maximized while the influence spreading in T is minimized.…”

Section: A Approximation Of the Spreading Incrementmentioning

confidence: 99%

“…(2) Updating δ V \T (v, S) According to (19),we have (w, r, u) . (25) Equation (25) illustrates that after adding x to the seed set, some paths where v is located between x and a node u in S should be removed in computing δ V \T (S, v).…”

Section: S∪{x}) Can Be Obtained By Incremental Calculations On the mentioning

confidence: 99%

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A Random Walk Approach for Avoiding Unwanted Users in Competitive Social Network

Liu

Chen

et al. 2020

IEEE Access

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How to detect influential customers to broadcast advertisements to the maximum range is a key issue in effective viral marketing. If multiple companies sell the same product in viral marketing campaigns, there was competition between them. There may exist some unwanted users who hold hostile opinions, and these users may have a negative effect upon receiving promotional information. The company does not want the advertising information to reach such unwanted users over a period of time. In such competitive advertising, how to propagate the positive influence of its product and avoid it reaching the unwanted users of competitors in the limited time is a critical problem for business product promotion. Motivated by the phenomenon, we study the influence maximization problem with limited unwanted users (IML) in the independent cascade (IC) model. To accelerate the process of the influence propagation simulation, we present a path sampling approach based on the random walk to simulate the process of influence propagation. To avoid the unwanted users, only the influences on the paths reaching the wanted users are calculated at each time step, and the influences reaching the unwanted users are ignored.To reduce the computation time, the paths of the random walk will be recorded to avoid repeated random walks in the subsequent seed selection. To find the optimal influential customers, we employ a greedy scheme to select the top-k most influential nodes as seed nodes. Experimental results over the real-world datasets show that the algorithm we presented can get wider effective influence spreading than other algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: A Approximation Of the Spreading Incrementmentioning

confidence: 99%

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A Random Walk Approach for Avoiding Unwanted Users in Competitive Social Network

Liu

Chen

et al. 2020

IEEE Access

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“…IM aims to find a small set of highly influential users such that they will cause the maximum influence spread in a social network [3,23,37,40]. To fit with different real application scenarios, many variants of the IM problem have been investigated recently, such as Topic-aware IM [5,17,29,30], Time-aware IM [14,20,39,47], Community-aware IM [28,42,45,48], Competitive IM [2,32,36,43], Multi-strategies IM [7,24], and Out-of-Home IM [51,53]. However, some critical characteristics of the IM study fail to be fully discussed in existing IM works.…”

Section: Introductionmentioning

confidence: 99%

Reconnecting the Estranged Relationships: Optimizing the Influence Propagation in Evolving Networks

Cai¹,

Liu²,

Sheng³

et al. 2022

Preprint

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Influence Maximization (IM), which aims to select a set of users from a social network to maximize the expected number of influenced users, has recently received significant attention for mass communication and commercial marketing. Existing research efforts dedicated to the IM problem depend on a strong assumption: the selected seed users are willing to spread the information after receiving benefits from a company or organization. In reality, however, some seed users may be reluctant to spread the information, or need to be paid higher to be motivated. Furthermore, the existing IM works pay little attention to capture user's influence propagation in the future period. In this paper, we target a new research problem, named Reconnecting Top-𝑙 Relationships (RT𝑙R) query, which aims to find 𝑙 number of previous existing relationships but being estranged later, such that reconnecting these relationships will maximize the expected number of influenced users by the given group in a future period. We prove that the RT𝑙R problem is NP-hard. An efficient greedy algorithm is proposed to answer the RT𝑙R queries with the influence estimation technique and the well-chosen link prediction method to predict the near future network structure. We also design a pruning method to reduce unnecessary probing from candidate edges. Further, a carefully designed order-based algorithm is proposed to accelerate the RT𝑙R queries. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness and efficiency of our proposed methods.

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“…(2) Random Walk Simulation (RW) and (3) Reverse Sketching (RS) methods are implemented for better efficiency, with accuracy guarantees. We compare them with (4) Independent Cascade (IC) and (5) Linear Threshold (LT) models-based seed selection, both coupled with IMM[191], considering only the edge weights and assuming that a user has only one chance to accept or reject a candidate.Multi-campaign versions MCIC and MCLT[29,160] also exist. However, in our problem setting, the opinions diffuse independently for different candidates and our algorithm selects seeds for the target candidate.…”

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confidence: 99%

Efficient optimization of network metrics in large uncertain graphs

Saha¹

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They gave me useful feedback and suggestions that helped me in many aspects of my research and to develop new research skills. Without their guidance, the achievements of my research work would never have been possible.Next, I would like to express my heartfelt gratitude to all my collaborators, namely Prof. Laks V.S. Lakshmanan, Dr. Francesco Bonchi, Dr. Yllka Velaj, Dr. Xiangyu Ke and Ruben Brokkelkamp, for their valuable contributions in the projects in which we have worked together. I also thank my friends and colleagues in my laboratory (DMAL) for their warm companionship and enlightening discussions. They include (but are not limited to) Dr.

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Influence Maximization for Complementary Goods

Cited by 11 publications

References 13 publications

A Random Walk Approach for Avoiding Unwanted Users in Competitive Social Network

A Random Walk Approach for Avoiding Unwanted Users in Competitive Social Network

Reconnecting the Estranged Relationships: Optimizing the Influence Propagation in Evolving Networks

Efficient optimization of network metrics in large uncertain graphs

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