Providing an Efficient Method to Identify Structural Balanced Social Network Charts using Data Mining Techniques

As social communications become widespread, social networks are expanding day by day, and the number of members is increasing. In this regard, one of the most important issues on social networks is the prediction of the link or the friend's suggestion, which is usually done using similarities among users. In the meantime, clustering methods are very popular, but because of the high convergence velocity dimensions, clustering methods are usually low. In this research, using spectral clustering and diminishing dimensions, reducing the amount of information, reduces clustering time and reduces computational complexity and memory. In this regard, the spectroscopic clustering method, using a balanced index, determines the number of optimal clusters, and then performs clustering on the normal values ​​of the normalized Laplace matrix. First, the clusters are divided into two parts and computed for each cluster of the harmonic distribution index. Each cluster whose index value for it is greater than 1 will be redistributed to two other clusters, and this will continue until the cluster has an index of less than 1. Finally, the similarity between the users within the cluster and between the clusters is calculated and the most similar people are introduced together. The best results for the Opinions, Google+ and Twitter data sets are 95.95, 86.44 and 95.45, respectively. The computational results of the proposed method and comparison with previous valid methods showed the superiority of the proposed approach

Advertising recommendation system based on dynamic data analysis on Turkish speaking Twitter users

Online okruženja, a posebno društvene mreže postala su snažna alternative objavljivanju oglasa. Za učinkovito oglašavanje važno je da se sadržaj poistovjećuje s očekivanjima ciljane publike. Uzimajući u obzir da se očekivanja mogu s vremenom promijeniti, potrebno je u realnom vremenu i dinamički prepoznati orijentaciju korisnika. U ovom su se radu u realnom vremenu analizirale poruke turskih korisnika Twittera i identificirala njihova trenutna očekivanja. U tu je svrhu dizajnirana web usluga koja analizira profil korisnika i daje oglase koji najbolje odgovaraju očekivanjima. Za filtriranje odgovarajućeg sadržaja oglašavanja korištena je metoda nazvana heuristička metoda odstranjivanja suvišnog (Heuristic Pruning Method - HPM). Razvijeni sustav je testiran na grupi volontera, aktivnih korisnika Twittera, a učinkovitost sustava se pokazala dobivenom povratnom informacijom-feedbackom.Online environments and especially social networks have become a great alternative to advertisement publishing. In order to accomplish effective advertising it is important that the contents coincide with the expectations of the target audience. Considering that expectations may change over time, it is required to identify the orientation of the users in real time and dynamically. In this study, the messages shared by Turkish Twitter users were analysed in real time and the instant expectations of the users have been identified. To perform this work, a web service was designed which analyses the user’s profile and presents the advertisements that suit best to expectations. A method called Heuristic Pruning Method (HPM) has been revealed in order to filter the most appropriate advertising content. The developed system has been tested on a voluntary participant group who actively uses Twitter, and the effectiveness of the system is demonstrated by the received feedback

Synergy Creation Of Users In Kousarnet Social Scientific Network Using Graph Based Clustering Methods

In recent years, the number of users of social networks has grown significantly. The big challenge for these networks’ audience is How to communicate with the people present on these networks. Friend recommender systems try to fix this challenge by offering suggestions. In this study, data from the social and scientific network of Kousarent were used. In this research, using 10 types of relationships between users without considering the friendship relationships, network graph created, and then by using 3 algorithms Louvain, Kmeans and Hierarchical graph clustering was performed to identify communities. Clusters obtained from Louvain's clustering algorithm had higher percentages of matching with friendships. Then, weights were calculated by genetic algorithm for each of 10 relationships and by applying Louvain clustering algorithm on the network graph, the highest percentage of matching with the optimal weight of each of the 10 relationships was obtained. In this case, the resulting clusters are optimal clusters containing the most similar users. So other users in the same cluster can be suggested as friends. The weight of the edges between the individuals in the graph was also used to prioritize the bids. At the end, the friend proposed method was evaluated and the percentage of suggested friends matched with the individual's true friends was calculated

Version-sensitive mobile app recommendation

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Analysis, Modeling, and Control of Dynamic Processes in Networks

Dynamic network processes have surrounded people for millennia. Information spread through social networks, alliance formation in financial and organizational networks, heat diffusion through material networks, and distributed synchronization in robotic networks are just a few examples. Network processes are studies along three dimensions: analysis of network processes through the data produced by them; designing complex plausible, yet, tractable mathematical models for network processes; and designing control mechanisms that would guide network processes towards desirable evolution patterns. This thesis advances the frontier of knowledge about network processes along each of these three dimensions, emphasizing applications to social networks.The first part of the thesis is dedicated to the design of a method for model-driven analysis of a polar opinion formation process in social networks. The core of the method is a distance measure quantifying the likelihood of a social network's transitioning between different states with respect to a chosen opinion dynamics model characterizing expected evolution of the network's state. I describe how to design such a distance measure relying upon the classical transportation problem, compute it in linear time, and use it in applications.In the second part of the thesis, I focus on designing a model for polar opinion formation in social networks, and define a class of non-linear models that capture the dependence of the users' opinion formation behavior upon the opinions themselves. The obtained models are connected to socio-psychological theories, and their behavior is theoretically analyzed employing tools from non-smooth analysis and a generalization of LaSalle Invariance Principle.The third part of the thesis targets the problem of defense against social control. While the existing socio-psychological theories as well as influence maximization techniques expose the opinion formation process in social networks to external attacks, I propose an algorithm that nullifies the effect of such attacks by strategically recommending a small number of new edges to the network's users. The optimization problem underlying the algorithm is NP-hard, and I provide a pseudo-linear time heuristic---drawing upon the theory of Markov chains---that solves the problem approximately and performs well in experiments