Approach For Influence Maximization Research Articles

A neighbour scale fixed approach for influence maximization in social networks

Influence maximization is currently a most extensively researched topic in social network analysis. Existing approaches tackle this task by either pursuing the real influence strength of a node or designing proper measurements for estimating it. The degree is a popularly adopted influence strength metric, based on which a variety of methods have been developed. Though with good efficiency, degree-based methods suffer unsatisfactory accuracy since this metric only covers a limited considered scale over the whole network of interest and also lacks discriminatory power. In this paper, we propose a novel influence maximization method, named Fixed Neighbour Scale (FNS), which extracts useful information from multiple levels of neighbours for a target node to estimate its influence strength, rather than only considering directly connected neighbours as in degree-based methods. To facilitate the implementation of FNS, we also present a centrality measurement termed FNS-dist, which estimates a node’s influence strength by summing its multi-level neighbours’ weights that are mainly determined by their distances to the target node. Experiments conducted on nine networks of different sizes and categories show that the proposed FNS method achieves excellent and stable performance compared with other algorithms based on designing metrics for measuring influence strength. We also exhibit that FNS-dist is a superior alternative centrality which is more proper and precise than the degree.

Hurst exponent based approach for influence maximization in social networks

Influence maximization in online social networks is a trending research area due to its use in many real-world domains. Influence maximization addresses the problem of identifying a k-size subset of nodes in a social network which can trigger a cascade of further adoptions, leading to maximum influence spread across the social network. In this paper, influence maximization has been proposed by combining a node’s connections and its actual past activity pattern. Analyzing node’s activity with respect to interaction frequency and self-similarity trend, provides a more realistic view of the node’s influence potential. Inspired by this concept, HAC-Rank algorithm has been proposed for identification of initial adopters based on both their connections and past behaviour. Furthermore, a Hurst-based Influence Maximization (HBIM) model for diffusion, wherein a node’s activation depends upon its connections and the self-similarity trend exhibited by its past activity, has also been proposed. For assessing the self-similarity trend in a node’s activity pattern, Hurst exponent (H) has been computed. Based on the results achieved, proposed algorithm has been found to perform better than other state-of-art algorithms for initial adopter identification.

Cancer driver gene discovery in transcriptional regulatory networks using influence maximization approach

Cancer driver genes (CDGs) are the genes whose mutations cause tumor growth. Several computational methods have been previously developed for finding CDGs. Most of these methods are sequence-based, that is, they rely on finding key mutations in genomic data to predict CDGs. In the present work, we propose iMaxDriver as a network-based tool for predicting driver genes by application of influence maximization algorithm on human transcriptional regulatory network (TRN). In the first step of this approach, the TRN is pruned and weighted by exploiting tumor-specific gene expression (GE) data. Then, influence maximization approach is used to find the influence of each gene. The top genes with the highest influence rate are selected as the potential driver genes. We compared the performance of our CDG prediction method with fifteen other computational tools, based on a benchmark of three different cancer types. Our results show that iMaxDriver outperforms most of the state-of-the-art algorithms for CDG prediction. Furthermore, iMaxDriver is able to correctly predict many CDGs that are overlooked by all previously published tools. Due to this relative orthogonality, iMaxDriver can be considered as a complementary approach to the sequence-based CDG prediction methods.

LAPSO-IM: A learning-based influence maximization approach for social networks

Online social networks play a pivotal role in the propagation of information and influence as in the form of word-of-mouth spreading. Influence maximization (IM) is a fundamental problem to identify a small set of individuals, which have maximal influence spread in the social network. IM problem is unfortunately NP-hard. It has been depicted that hill-climbing greedy approach gives a good approximation guarantee. However, it is inefficient to run a greedy approach on large-scale social networks. In this paper, a local influence evaluation function is presented for optimizing IM problem. The local influence evaluation function provides a reliable expected diffusion value of influence spread under the linear threshold, independent and weighted cascade models. To optimize local influence evaluation function, a learning automata based discrete particle swarm optimization (LAPSO-IM) algorithm is proposed. LAPSO-IM redefines the update rule of particle’s velocity based on learning automata action to overcome the weakness of premature convergence. The experimental results on six real-world social networks show that the proposed algorithm is more effective than base algorithm DPSO with same the level of efficiency and more time-efficient than IMLA with approximate influence spread.

Identification of influential users in social networks based on users’ interest

Ever increasing popularity social networks has attracted many companies to use them for viral marketing. Identification of influential users for spreading news and marketing is a major challenge in viral marketing. In most of the existing influence maximization approaches, the topological locations of nodes on the network has been considered as a criterion to determine their influentiality. They have mainly neglected users’ interest in the marketing message. Although a number of existing works have considered interests of users, they have not used any criterion to specify the interest. This manuscript proposes a novel criterion to measure the interest of users in the marketing messages. We then propose a novel algorithm to obtain the set of the most influential users. Experimental results on real-world and synthetic networks reveal effectiveness of the proposed method as compared to the existing state-of-the-art influence maximization algorithms.

A reversed node ranking approach for influence maximization in social networks

Influence maximization, i.e. to maximize the influence spread in a social network by finding a group of influential nodes as small as possible, has been studied widely in recent years. Many methods have been developed based on either explicit Monte Carlo simulation or scoring systems, among which the former perform well yet are very time-consuming while the latter ones are efficient but sensitive to different spreading models. In this paper, we propose a novel influence maximization algorithm in social networks, named Reversed Node Ranking (RNR). It exploits the reversed rank information of a node and the effects of its neighbours upon this node to estimate its influence power, and then iteratively selects the top node as a seed node once the ranking reaches stable. Besides, we also present two optimization strategies to tackle the rich-club phenomenon. Experiments on both Independent Cascade (IC) model and Weighted Cascade (WC) model show that our proposed RNR method exhibits excellent performance and outperforms other state-of-the-arts. As a by-product, our work also reveals that the IC model is more sensitive to the rich-club phenomenon than the WC model.

IM-GSO: A Community Directed Group Search Optimization Approach for Influence Maximization

Influence maximization is a fundamental problem in the study of complex relationship networks, such as viral marketing in business application areas. It is directed towards extracting a minimal (or k-sized) subset of most influential nodes with largest cascading effect across the network as per seeding budget. The problem is categorized as NP hard and hence greedy/heuristic techniques are extensively studied in the literature for generating reasonably acceptable solutions. This article proposes a novel nature based heuristic optimization algorithm IM-GSO to dynamically evolve near to optimal K-sized influential seed nodes for varied structural real world networks. IM-GSO smartly incorporates hidden structural patterns like communities, node degrees, betweenness and similarities for efficient candidate population generation. This smartly initialized population is then evolved using a discrete adaption of Group Search Optimization (GSO) algorithm. Correctness of IM-GSO is verified by optimizing two prominent spread estimation functions SIMPATH and MAGA, on varied sized (small/medium/large) networks. Detailed experimental evaluations by execution of 10,000 Monte Carlo simulations under Information Cascade (IC) model indicates a significantly high influence spread for IM-GSO seeds in contrast to standard heuristics techniques.

An efficient and effective hop-based approach for influence maximization in social networks

Influence maximization in social networks is a classic and extensively studied problem that targets at selecting a set of initial seed nodes to spread the influence as widely as possible. However, it remains an open challenge to design fast and accurate algorithms to find solutions in large-scale social networks. Prior Monte Carlo simulation-based methods are slow and not scalable, while other heuristic algorithms do not have any theoretical guarantee and they have been shown to produce poor solutions for quite some cases. In this paper, we propose hop-based algorithms that can be easily applied to billion-scale networks under the commonly used Independent Cascade and Linear Threshold influence diffusion models. Moreover, we provide provable data-dependent approximation guarantees for our proposed hop-based approaches. Experimental evaluations with real social network datasets demonstrate the efficiency and effectiveness of our algorithms.

Influence analysis: A survey of the state-of-the-art

Online social networks have seen an exponential growth in number of users and activities recently. The rapid proliferation of online social networks provides rich data and infinite possibilities for us to analyze and understand the complex inherent mechanism which governs the evolution of the new online world. This paper summarizes the state-of-art research results on social influence analysis in a broad sense. First, we review the development process of influence analysis in social networks based on several basic conceptions and features in a social aspect. Then the online social networks are discussed. After describing the classical models which simulate the influence spreading progress, we give a bird's eye view of the up-to-date literatures on influence diffusion models and influence maximization approaches. Third, we present the applications including web services, marketing, and advertisement services which based on the influence analysis. At last, we point out the research challenges and opportunities in this area for both industry and academia reference.

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

A two-stage stochastic programming approach for influence maximization in social networks

We consider stochastic influence maximization problems arising in social networks. In contrast to existing studies that involve greedy approximation algorithms with a 63% performance guarantee, our work focuses on solving the problem optimally. To this end, we introduce a new class of problems that we refer to as two-stage stochastic submodular optimization models. We propose a delayed constraint generation algorithm to find the optimal solution to this class of problems with a finite number of samples. The influence maximization problems of interest are special cases of this general problem class. We show that the submodularity of the influence function can be exploited to develop strong optimality cuts that are more effective than the standard optimality cuts available in the literature. Finally, we report our computational experiments with large-scale real-world datasets for two fundamental influence maximization problems, independent cascade and linear threshold, and show that our proposed algorithm outperforms the greedy algorithm.

Real-time influence maximization on dynamic social streams

Influence maximization (IM), which selects a set of k users (called seeds) to maximize the influence spread over a social network, is a fundamental problem in a wide range of applications such as viral marketing and network monitoring. Existing IM solutions fail to consider the highly dynamic nature of social influence, which results in either poor seed qualities or long processing time when the network evolves. To address this problem, we define a novel IM query named Stream Influence Maximization (SIM) on social streams. Technically, SIM adopts the sliding window model and maintains a set of k seeds with the largest influence value over the most recent social actions. Next, we propose the Influential Checkpoints (IC) framework to facilitate continuous SIM query processing. The IC framework creates a checkpoint for each window shift and ensures an ε -approximate solution. To improve its efficiency, we further devise a Sparse Influential Checkpoints (SIC) framework which selectively keeps O (log N /β checkpoints for a sliding window of size N and maintains an ε(1−β)/2-approximate solution. Experimental results on both real-world and synthetic datasets confirm the effectiveness and efficiency of our proposed frameworks against the state-of-the-art IM approaches.

Learning from Bees: An Approach for Influence Maximization on Viral Campaigns

Maximisation of influence propagation is a key ingredient to any viral marketing or socio-political campaigns. However, it is an NP-hard problem, and various approximate algorithms have been suggested to address the issue, though not largely successful. In this paper, we propose a bio-inspired approach to select the initial set of nodes which is significant in rapid convergence towards a sub-optimal solution in minimal runtime. The performance of the algorithm is evaluated using the re-tweet network of the hashtag #KissofLove on Twitter associated with the non-violent protest against the moral policing spread to many parts of India. Comparison with existing centrality based node ranking process the proposed method significant improvement on influence propagation. The proposed algorithm is one of the hardly few bio-inspired algorithms in network theory. We also report the results of the exploratory analysis of the network kiss of love campaign.

A Query Approach for Influence Maximization on Specific Users in Social Networks

Influence maximization is introduced to maximize the profit of viral marketing in social networks. The weakness of influence maximization is that it does not distinguish specific users from others, even if some items can be only useful for the specific users. For such items, it is a better strategy to focus on maximizing the influence on the specific users. In this paper, we formulate an influence maximization problem as query processing to distinguish specific users from others. We show that the query processing problem is NP-hard and its objective function is submodular. We propose an expectation model for the value of the objective function and a fast greedy-based approximation method using the expectation model. For the expectation model, we investigate a relationship of paths between users. For the greedy method, we work out an efficient incremental updating of the marginal gain to our objective function. We conduct experiments to evaluate the proposed method with real-life datasets, and compare the results with those of existing methods that are adapted to the problem. From our experimental results, the proposed method is at least an order of magnitude faster than the existing methods in most cases while achieving high accuracy.

A new approach for influence maximization in complex networks

Influence maximization modeling and analyzing is a critical issue in social network analysis in a complex network environment, and it can be significantly beneficial to both theory and real life. Given a fixed number k, how to find the set size k which has the greatest influencing scope is a combinatory optimization problem that has been proved to be NP-hard by Kempe et al. (2003). State-of-the-art random algorithm, although it is computation efficient, yields the worst performance; on the contrary, the well-studied greedy algorithms can achieve approximately optimal performance but its computing complexity is prohibitive for large social network; meanwhile, these algorithms should first acquire the global information (topology) of the network which is impractical for the colossal and forever changing network. We propose a new algorithm for influence maximization computing-RMDN and its improved version RMDN++. RMDN uses the information of a randomly chosen node and its nearest neighboring nodes which can avoid the procedure of knowing knowledge of the whole network. This can greatly accelerate the computing process, but its computing complexity is limited to the order of O(klog(n)). We use three different real-life datasets to test the effectiveness and efficiency of RMDN in IC model and LT model respectively. Result shows that RMDN has a comparable performance as the greedy algorithms, but obtains orders of magnitude faster according to different network; in the meantime, we have systematically and theoretically studied and proved the feasibility of our method. The wider applicability and stronger operability of RMDN may also shed light on the profound problem of influence maximization in social network.

A Novel Community-Based Approach for Influence Maximization in Social Network

Our study concerns an important current problem, that of influence maximization in social network. This problem has received significant attention from the researchers recently, driven by many potential applications such as viral marketing and sales promotion. It is a fundamental issue to find a subset of key individuals in a social network such that targeting them initially (e.g. to adopt a new product) will maximize the spread of the influence (further adoptions of the new product). However, the problem of finding the most key nodes is unfortunately NP-hard. It has been shown that a greedy algorithm with provable approximation guarantees can give good approximation. However, it is computationally expensive, if not prohibitive, to run the greedy algorithm on a large social network. Based on the community structure property of social network, a cooperative game theoretic algorithm CGINA to find key nodes is proposed. The proposed algorithm encompasses two stages. Firstly, we detect the community structure of the social network with the topological structure and information diffusion model. In this paper, we adopt the algorithm 2 in [4]. Then, we will find key nodes in communities. In this paper, we think of the information diffusion in the whole network as a cooperative game with transferable utility. The communities of the network happen to be the players in this cooperative game. With the solution concept Shapley value in game theory, we allocate the number of key nodes to discover in the community. Moreover, different from the existing relevant literature, in my opinion, the key nodes two parts. One is composed of “bridge” nodes, which contact other communities densely, and are easy to disseminate information across communities, the other is composed of “influential” nodes, which are at the core of the community, in close touch with other nodes, and can diffuse information quickly within the community. To adjust the ratio of these two kinds of nodes in the same community, a heuristic factor l is introduced. Empirical studies on a large social network show that our algorithm is efficient and powerful.