Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give each source. We cannot expect each user to know the trustworthiness of each source, nor would we want to assign top-down or global credibility values due to the subjective nature of trust. We tackle this problem by employing a web of trust, in which each user maintains trusts in a small number of other users. We then compose these trusts into trust values for all other users. The result of our computation is not an agglomerate "trustworthiness" of each user. Instead, each user receives a personalized set of trusts, which may vary widely from person to person. We define properties for combination functions which merge such trusts, and define a class of functions for which merging may be done locally while maintaining these properties. We give examples of specific functions and apply them to data from Epinions and our BibServ bibliography server. Experiments confirm that the methods are robust to noise, and do not put unreasonable expectations on users. We hope that these methods will help move the Semantic Web closer to fulfilling its promise.

Trust management for the Semantic Web

Given a social network with diffusion probabilities as edge weights and a positive integer k, which k nodes should be chosen for initial injection of information to maximize the influence in the network? This problem is popularly known as the Social Influence Maximization Problem (SIM Problem). This is an active area of research in computational social network analysis domain, since one and half decades or so. Due to its practical importance in various domains, such as viral marketing, target advertisement and personalized recommendation, the problem has been studied in different variants, and different solution methodologies have been proposed over the years. This paper presents a survey on the progress in and around SIM Problem. At last, it discusses current research trends and future research directions as well.

A survey on influence maximization in a social network

Given a social network with diffusion probabilities as edge weights and an integer k, which k nodes should be chosen for initial injection of information to maximize influence in the network? This problem is known as Target Set Selection in a social network (TSS Problem) and more popularly, Social Influence Maximization Problem (SIM Problem). This is an active area of research in computational social network analysis domain since one and half decades or so. Due to its practical importance in various domains, such as viral marketing, target advertisement, personalized recommendation, the problem has been studied in different variants, and different solution methodologies have been proposed over the years. Hence, there is a need for an organized and comprehensive review on this topic. This paper presents a survey on the progress in and around TSS Problem. At last, it discusses current research trends and future research directions as well.

A Survey on Influence Maximization in a Social Network

TIFIM: A Two-stage Iterative Framework for Influence Maximization in Social Networks

Influence maximization problem aims to select a subset of k most influential nodes from a given network such that the spread of influence triggered by the seed set will be maximum. Greedy based algorithms are time-consuming to approximate the expected influence spread of given node set accurately and not well scalable to large-scale networks especially when the propagation probability is large. Conventional heuristics based on network topology or confined diffusion paths tend to suffer from the problem of low solution accuracy or huge memory cost. In this paper an effective discrete shuffled frog-leaping algorithm (DSFLA) is proposed to solve influence maximization problem in a more efficient way. Novel encoding mechanism and discrete evolutionary rules are conceived based on network topology structure for virtual frog population. To facilitate the global exploratory solution, a novel local exploitation mechanism combining deterministic and random walk strategies is put forward to improve the suboptimal meme of each memeplex in the frog population. The experimental results of influence spread in six real-world networks and statistical tests show that DSFLA performs effectively in selecting targeted influential seed nodes for influence maximization and is superior than several state-of-the-art alternatives.

A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks

Influence maximization is a classic optimization problem studied in the area of social network analysis and viral marketing. Given a network, it is defined as the problem of finding k seed nodes so that the influence spread of the network can be optimized. Kempe et al. have proved that this problem is NP hard and the objective function is submodular, based on which a greedy algorithm was proposed to give a near-optimal solution. However, this simple greedy algorithm is time consuming, which limits its application on large-scale networks. Heuristic algorithms generally cannot provide any performance guarantee. To solve this problem, in this paper we propose CoFIM, a community-based framework for influence maximization on large-scale networks. In our framework the influence propagation process is divided into two phases: (i) seeds expansion; and (ii) intra-community propagation. The first phase is the expansion of seed nodes among different communities at the beginning of diffusion. The second phase is the influence propagation within communities which are independent of each other. Based on the framework, we derive a simple evaluation form of the total influence spread which is submodular and can be efficiently computed. Then we further propose a fast algorithm to select the seed nodes. Experimental results on synthetic and nine real-world large datasets including networks with millions of nodes and hundreds of millions of edges show that our algorithm achieves competitive results in influence spread as compared with state-of-the-art algorithms and it is much more efficient in terms of both time and memory usage.

CoFIM: A community-based framework for influence maximization on large-scale networks

The study of influence maximization (IM) has attracted many scholars in recent years due to its import practical values. Given a social network, it aims at finding a subset of k individuals as seed nodes which can trigger the maximum influence cascade through the network under a predefined diffusion model. Kempe et al. first formulated influence maximization as a discrete optimization problem and proved its NP-hardness and submodularity, based on which they further proposed a greedy approach with guaranteed solution accuracy. Unfortunately, the greedy algorithm was also known for its extremely low time efficiency. To solve this problem more efficiently, many research works were proposed in recent years. However, these studies either make sacrifices in solution accuracy or require huge memory consumption. Besides, only a handful of research works can handle mega-scale networks with millions of nodes and edges. To solve this problem both efficiently and effectively, in this paper we propose IMPC: an influence maximization framework based on multi-neighbor potential in community networks. In our approach the influence diffusion process is divided into two phases: (i) multi-neighbor potential based seeds expansion; and (ii) intra-community influence propagation. Based on this framework we derive an objective function to evaluate the overall influence as a combination of the influence during the two phases. We theoretically prove that the objective function is submodular and design an efficient greedy algorithm to find the seed nodes. We evaluate the performance of our framework on eight real-world networks which scale up to millions of nodes and hundreds of millions of edges. Experimental results show that our approach can significantly outperform other state-of-the-art algorithms in terms of time and space efficiency with no compromise on solution accuracy.

IMPC: Influence maximization based on multi-neighbor potential in community networks

The problem of influence maximization (IM) has been extensively studied in recent years and has many practical applications such as social advertising and viral marketing. Given the network and diffusion model, IM aims to find an influential set of seed nodes so that targeting them as diffusion sources will trigger the maximum cascade of influenced individuals. The largest challenge of the IM problem is its NP-hardness, and most of the existing approaches are with polynomial time complexity, making themselves unscalable to very large networks. To address this issue, in this paper, we propose LAIM: a linear time iterative approach for efficient IM on large-scale networks. Our framework has two steps: 1) influence approximation and 2) seed set selection. In the first step, we propose an iterative algorithm to compute the local influence of a node based on a recursive formula and use the local influence to approximate its global influence. In the second step, the $k$ influential seed nodes are mined based on the approximated influence in the first step. Based on our model, we theoretically prove that the proposed approach has linear time and space complexity. We further accelerate our algorithm with simple modifications and propose its fast version. Experimental results on eight real-world large-scale networks exhibit the superiority of our approach over the state-of-the-art methods in terms of both effectiveness and efficiency.

LAIM: A Linear Time Iterative Approach for Efficient Influence Maximization in Large-Scale Networks

Influence maximization is an extensively studied optimization problem aiming at finding the best k seed nodes in a network such that they can influence the maximum number of individuals Traditional heuristic or shortest path based methods either cannot provide any performance guarantee or require huge amount of memory usage, making themselves ineffective in real world applications In this paper, we propose MSIM: a multi-selector framework which combines the intelligence of different existing algorithms Our framework consists of three layers: (i) the selector layer; (ii) the combiner layer, and (iii) the evaluator layer The first layer contains different selectors and each selector can be arbitrary existing influence maximization algorithm The second layer contains several combiners and combines the output of the first layer in different ways The third layer evaluates the candidates elected by the second layer to find the best seed nodes in an iterative manner Experimental results on five real world datasets show that our framework always effectively finds better seed nodes than other state-of-the-art algorithms Our work provides a new perspective to the study of influence maximization

Effective Influence Maximization Based on the Combination of Multiple Selectors

Influence maximization is the problem of finding k seed nodes in a given network as information sources so that the influence cascade can be maximized. To solve this problem both efficiently and effectively, in this paper we propose LAIM: a linear time algorithm for influence maximization in large-scale social networks. Our LAIM algorithm consists of two parts: (1) influence computation; and (2) seed nodes selection. The first part approximates the influence of any node using its local influence, which can be efficiently computed with an iterative algorithm. The second part selects seed nodes in a greedy manner based on the results of the first part. We theoretically prove that the time and space complexities of our algorithm are proportional to the network size. Experimental results on six real-world datasets show that our approach significantly outperforms other state-of-the-art algorithms in terms of influence spread, running time and memory usage.

Hongchun Wu

Papers

CoFIM: A community-based framework for influence maximization on large-scale networks

IMPC: Influence maximization based on multi-neighbor potential in community networks

LAIM: A Linear Time Iterative Approach for Efficient Influence Maximization in Large-Scale Networks

Effective Influence Maximization Based on the Combination of Multiple Selectors

A Linear Time Algorithm for Influence Maximization in Large-Scale Social Networks.