Results 1 -
7 of
7
Influence spreading path and its application to the time constrained social influence maximization problem and beyond
, 2014
"... Abstract—Influence maximization is a fundamental research problem in social networks. Viral marketing, one of its applications, is to get a small number of users to adopt a product, which subsequently triggers a large cascade of further adoptions by utilizing “Word-of-Mouth ” effect in social networ ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
(Show Context)
Abstract—Influence maximization is a fundamental research problem in social networks. Viral marketing, one of its applications, is to get a small number of users to adopt a product, which subsequently triggers a large cascade of further adoptions by utilizing “Word-of-Mouth ” effect in social networks. Time plays an important role in the influence spread from one user to another and the time needed for a user to influence another varies. In this paper, we propose the time constrained influence maximization problem. We show that the problem is NP-hard, and prove the monotonicity and submodularity of the time constrained influence spread function. Based on this, we develop a greedy algorithm. To improve the algorithm scalability, we propose the concept of Influence Spreading Path in social networks and develop a set of new algorithms for the time constrained influence maximization problem. We further parallelize the algorithms for achieving more time savings. Additionally, we generalize the proposed algorithms for the conventional influence maximization problem without time constraints. All of the algorithms are evaluated over four public available datasets. The experimental results demonstrate the efficiency and effectiveness of the algorithms for both conventional influence maximization problem and its time constrained version. Index Terms—Influence spreading path, influence maximization, social network, large scale, time constrained 1
Influence Maximization with Bandits
"... We consider the problem of influence maximization in networks, maximizing the number of people that become aware of a product by finding the ‘best ’ set of ‘seed ’ users to expose the product to. Most prior work on this topic assumes that we know the probability of each user influencing each other u ..."
Abstract
- Add to MetaCart
(Show Context)
We consider the problem of influence maximization in networks, maximizing the number of people that become aware of a product by finding the ‘best ’ set of ‘seed ’ users to expose the product to. Most prior work on this topic assumes that we know the probability of each user influencing each other user, or we have data that lets us estimate these influences. However, this information is typically not available or is difficult to obtain. To avoid this assumption, we adopt a combi-natorial multi-armed bandit paradigm that estimates the influence probabilities as we sequentially try different seed sets. We establish bounds on the performance of this procedure under the existing edge-level feedback mechanism as well as a novel and more realistic node-level feedback mechanism. Beyond our theoretical results, we describe a practical implementation and experimentally demonstrate its efficiency and effectiveness on four real datasets. 1
Node Immunization on Large Graphs: Theory and Algorithms
"... Abstract—Given a large graph, like a computer communication network, which k nodes should we immunize (or monitor, or remove), to make it as robust as possible against a computer virus attack? This problem, referred to as the Node Immunization problem, is the core building block in many high-impact ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract—Given a large graph, like a computer communication network, which k nodes should we immunize (or monitor, or remove), to make it as robust as possible against a computer virus attack? This problem, referred to as the Node Immunization problem, is the core building block in many high-impact applications, ranging from public health, cybersecurity to viral marketing. A central component in Node Immunization is to find the best k bridges of a give graph. In this setting, we typically want to determine the relative importance of a node (or a set of nodes) within the graph, for example, how valuable (as a bridge) a person or a group of persons is in a social network. First of all, we propose a novel ‘bridging ’ score ∆λ, inspired by immunology, and we show that its results agree with intuition for several realistic settings. Since the straightforward way to compute ∆λ is computationally intractable, we then focus on the computational issues and propose a surprisingly efficient way (O(nk2 + m)) to estimate it. Experimental results on real graphs show that (1) the proposed ‘bridging ’ score gives mining results consistent with intuition; and (2) the proposed fast solution is up to 7 orders of magnitude faster than straightforward alternatives.
Al-Imam Muhammad Ibn Saud Islamic University
"... Abstract—Current obstacles in the study of social media marketing include dealing with massive data and real-time updates have motivated to contribute solutions that can be adopted for viral marketing. Since information diffusion and social networks are the core of viral marketing, this article aims ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract—Current obstacles in the study of social media marketing include dealing with massive data and real-time updates have motivated to contribute solutions that can be adopted for viral marketing. Since information diffusion and social networks are the core of viral marketing, this article aims to investigate the constellation of diffusion methods for viral marketing. Studies on diffusion methods for viral marketing have applied different computational methods, but a systematic investigation of these methods has limited. Most of the literature have focused on achieving objectives such as influence maxi-mization or community detection. Therefore, this article aims to conduct an in-depth review of works related to diffusion for viral marketing. Viral marketing has applied to business-to-consumer transactions but has seen limited adoption in business-to-business transactions. The literature review reveals a lack of new diffusion methods, especially in dynamic and large-scale networks. It also offers insights into applying various mining methods for viral marketing. It discusses some of the challenges, limitations, and future research directions of information diffusion for viral marketing. The article also introduces a viral marketing informa-tion diffusion model. The proposed model attempts to solve the dynamicity and large-scale data of social networks by adopting incremental clustering and a stochastic differential equation for business-to-business transactions. Keywords—information diffusion; viral marketing; social media marketing; social networks I.
Creative Commons CC-BY 4.0 OPEN ACCESS
, 2015
"... Declarations can be found on page 29 DOI 10.7717/peerj-cs.42 Copyright 2016 Topirceanu et al. Distributed under ..."
Abstract
- Add to MetaCart
(Show Context)
Declarations can be found on page 29 DOI 10.7717/peerj-cs.42 Copyright 2016 Topirceanu et al. Distributed under
