M. Taherinia; M. Esmaeili; B. Minaei Bidgoli
Abstract
The Influence Maximization Problem in social networks aims to find a minimal set of individuals to produce the highest influence on other individuals in the network. In the last two decades, a lot of algorithms have been proposed to solve the time efficiency and effectiveness challenges of this NP-Hard ...
Read More
The Influence Maximization Problem in social networks aims to find a minimal set of individuals to produce the highest influence on other individuals in the network. In the last two decades, a lot of algorithms have been proposed to solve the time efficiency and effectiveness challenges of this NP-Hard problem. Undoubtedly, the CELF algorithm (besides the naive greedy algorithm) has the highest effectiveness among them. Of course, the CELF algorithm is faster than the naive greedy algorithm (about 700 times). This superiority has led many researchers to make extensive use of the CELF algorithm in their innovative approaches. However, the main drawback of the CELF algorithm is the very long running time of its first iteration. Because it has to estimate the influence spread for all nodes by expensive Monte-Carlo simulations, similar to the naive greedy algorithm. In this paper, a heuristic approach is proposed, namely Optimized-CELF algorithm, to improve this drawback of the CELF algorithm by avoiding unnecessary Monte-Carlo simulations. It is found that the proposed algorithm reduces the CELF running time, and subsequently improves the time efficiency of other algorithms that employed the CELF as a base algorithm. Experimental results on the wide spectral of real datasets showed that the Optimized-CELF algorithm provided better running time gain, about 88-99% and 56-98% for k=1 and k=50, respectively, compared to the CELF algorithm without missing effectiveness.
N. Taghvaei; B. Masoumi; M. R. Keyvanpour
Abstract
In general, humans are very complex organisms, and therefore, research into their various dimensions and aspects, including personality, has become an attractive subject of research. With the advent of technology, the emergence of a new kind of communication in the context of social networks has also ...
Read More
In general, humans are very complex organisms, and therefore, research into their various dimensions and aspects, including personality, has become an attractive subject of research. With the advent of technology, the emergence of a new kind of communication in the context of social networks has also given a new form of social communication to humans, and the recognition and categorization of people in this new space have become a hot topic of research that has been challenged by many researchers. In this paper, considering the Big Five personality characteristics of individuals, first, categorization of related work is proposed, and then a hybrid framework based on Fuzzy Neural Networks (FNN), along with, Deep Neural Networks (DNN) has been proposed that improves the accuracy of personality recognition by combining different FNN-classifiers with DNN-classifier in a proposed two-stage decision fusion scheme. Finally, a simulation of the proposed approach is carried out. The proposed approach is using the structural features of Social Networks Analysis (SNA), along with a linguistic analysis (LA) feature extracted from the description of the activities of individuals and comparison with the previous similar researches. The results, well-illustrated the performance improvement of the proposed framework up to 83.2 % of average accuracy on myPersonality dataset.
