Mining Influential Users in Social Network

The appearance of social networks has been one of the most important events in the last decade. A social network is a network of interaction and communication whose nodes are its members and its edges are the relation between them. An outstanding concept in social networks is the concept of social influence which is of great significance. Social influence is the behavioral transformation in a person, caused by his/her relationship with other people, organizations and the network. Today, the concept of social influence is used in viral marketing. The aim of viral marketing is to find a small subset of influential users in the social networks for marketing a product. The aim of this paper is to offer an algorithm in order to choose expert and influential users in social networks. This algorithm uses leader discovery method in social networks for choosing expert and influential users. This method chooses those users as the expert and influential leaders of the social networks, who are influential among the other members and also those users that have enough specialty and knowledge about the marketing product. Consequently, the process of selecting expert and influential users for the algorithm would be quick and also the selected leaders are influential and expert enough to do marketing and advertising in the social network.

Identification of influential users by neighbors in online social networks

Social networks (SNs) have become an integral part of contemporary life, as they are increasingly used as a basic means for communication with friends, sharing of opinions and staying up to date with news and current events. The general increase in the usage and popularity of social media has led to an explosion of available data, which creates opportunities for various kinds of utilization, such as predicting, finding or even creating trends. We are thus interested in exploring the following questions: (a) Which are the most influential - popular internet publications posted in SNs, for a specific topic? (b) Which members of SNs are experts or influential regarding a specific topic? Our approach towards answering the above questions is based on the functionality of hashtags, which we use as topic indicators for posts, and on the assumption that a specific topic is represented by multiple hashtags. We present a neighborhood-based recommender system, which we have implemented using collaborative filtering algorithms in order to (a) identify hashtags, urls and users related with a specific topic, and (b) combine them with SN-based metrics in order to address the aforementioned questions in Twitter. The recommender system is built on top of Apache Spark framework in order to achieve optimal scaling and efficiency. For the verification of our system we have used data sets mined from Twitter and tested the extracted results for influential users and urls concerning specific topics in comparison with the influence scores produced by a state of the art influence estimation tool for SNs. Finally, we present and discuss the results regarding two distinct topics and also discuss the offered and potential utility of our system.

Closed frequent similar pattern mining: Reducing the number of frequent similar patterns without information loss

Systems that classify influential users in social networks have been used frequently and are referenced in scientific papers and in the media as an ideal standard of evaluation of influence in the Twitter social network. We consider such systems of evaluation to be complex and subjective, and we therefore suspect that they are vulnerable and easy to manipulate. Based on this, we performed experiments and analysis of two systems for ranking influence: Klout and Twitalyzer. We created simple robots capable of interacting by means of Twitter accounts, and we measured how influent they were. Our results show that it is possible to become influential through simple strategies. This suggests that the systems do not have ideal means to measure and classify influence.

The research of Influence maximization (IM) has always been a hot research topic in network analysis, which aims to find the most influential users in social networks to maximize the reach of influence. In recent year, many studies have focused on the problem of IM to improve efficiency by taking advantage of the small-scale community structures. However, the existing community-based methods only consider the number of nodes in a community and ignore the density of edge connections in a community. In addition, existing method can only be applied to non-overlapping community structures. In this paper, we propose community closeness-based influence maximization algorithm (CCIM) to select most influential nodes. CCIM considers the influence of point-to-point and point-to-community, reflecting the micro-level and meso-level influence. The experimental results on synthetic and three real datasets verify CCIM outperforms the state-of-the-art baselines.

Identifying the influential spreaders is an important issue in understanding and controlling the spreading processes in social networks. The key to the influential spreader identification problem is how to evaluate the spreading ability of the nodes. Centralities such as degree, semi-local centrality, betweenness, closeness, k-shell are usually used as evaluation metric. However, we observe that these centralities are sensitive not only to the spreading probability, but also to the network structure. We are not sure which centrality is efficient when we face a new network. In this paper, we propose a robust method named Adjustable multi-Hops Spreading (AHS). In AHS, we refine the influence of a node into direct influence and indirect influence, and then integrate them with a adjustable parameter. The experimental results in both real social networks and artificial networks show that AHS outperforms other centralities in effectiveness, robustness and the distinguish ability.

Discovering top-k influential users plays a central role in many social network applications. In this paper, we study a challenging problem of discovering item-based top-k influential users in social networks. Specifically, we present a dynamic selection approach (referred to as Item-based top-K influential user Discovering Approach, IDA for short), to identify the top-k influential users for a given item based on real-world diffusion traces and on-line relationships. In particular, IDA first softly divides users involved in a diffusion trace into different communities by topic, and ranks users' influence degrees in these topic communities with activeness, follower-counts, and follower participation-rates (including forwards and comments). In doing so, the top-K influential users for a given item can be obtained w.r.t. different topic communities. Experimental results on real world data sets demonstrate the performance of our approach.

Frequent pattern mining is a classic data mining technique, generally applicable to a wide range of application domains, and a mature area of research. The fundamental challenge arises from the combinatorial nature of frequent itemsets, scaling exponentially with respect to the number of unique items. Apriori-based and FPTree-based algorithms have dominated the space thus far. Initial phases of this research relied on the Apriori algorithm and utilized a distributed computing environment; we proposed the Cartesian Scheduler to manage Apriori's candidate generation process. To address the limitation of bottom-up frequent pattern mining algorithms such as Apriori and FPGrowth, we propose the Frequent Hierarchical Pattern Tree (FHPTree): a tree structure and new frequent pattern mining paradigm. The classic problem is redefined as frequent hierarchical pattern mining where the goal is to detect frequent maximal pattern covers. Under the proposed paradigm, compressed representations of maximal patterns are mined using a top-down FHPTree traversal, FHPGrowth, which detects large patterns before their subsets, thus yielding significant reductions in computation time. The FHPTree memory footprint is small; the number of nodes in the structure scales linearly with respect to the number of unique items. Additionally, the FHPTree serves as a persistent, dynamic data structure to index frequent patterns and enable efficient searches. When the search space is exponential, efficient targeted mining capabilities are paramount; this is one of the key contributions of the FHPTree. This dissertation will demonstrate the performance of FHPGrowth, achieving a 300x speed up over state-of-the-art maximal pattern mining algorithms and approximately a 2400x speedup when utilizing FHPGrowth in a distributed computing environment. In addition, we allude to future research opportunities, and suggest various modifications to further optimize the FHPTree and FHPGrowth. Moreover, the methods we offer will have an impact on other data mining research areas including contrast set mining as well as spatial and temporal mining.

Klout, a famous App, could measure people's social network influence power. Klout score is measured according to the data from past 90 days and an individual who has high Klout score is thought as having high social influence power. Lots of businesses or organizations like to hire high Klout score people to help them to diffuse their brand images. However, Klout score cannot tell us who has high influence power in a specific short time period. For example, it is possible that some of the users might always have high influence power on Monday or on Monday morning. These time-dependent influential users probably have low Klout scores in average but have high influence power in some specific time periods. Businesses should not just know who are the high Klout score users but also they should identify who are the time-dependent influential users because all of them may have some sort of power to influence other users' buying decisions. In this study, a framework based on frequent pattern mining is proposed to find the time-dependent influential users. First of all, the framework will divide a predefined long time period into successive short time segments and then influential transactions that contain Facebook fans' influence power data will be defined in each time segment. From the frequent patterns, the proper time for time-dependent influence users to spread information can be found. A theoretical experiment is given to verify the effectiveness of the proposed framework.

Grouping users in social networks is an important process that improves matching and recommendation activities in social networks. The data mining methods of clustering can be used in grouping the users in social networks. However, the existing general purpose clustering algorithms perform poorly on the social network data due to the special nature of users' data in social networks. One main reason is the constraints that need to be considered in grouping users in social networks. Another reason is the need of capturing large amount of information about users which imposes computational complexity to an algorithm. In this paper, we propose a scalable and effective constraint-based clustering algorithm based on a global similarity measure that takes into consideration the users' constraints and their importance in social networks. Each constraint's importance is calculated based on the occurrence of this constraint in the dataset. Performance of the algorithm is demonstrated on a dataset obtained from an online dating website using internal and external evaluation measures. Results show that the proposed algorithm is able to increases the accuracy of matching users in social networks by 10% in comparison to other algorithms.

Nowadays, the user linkage or link prediction task is usually based on user profile or some sensitive data (i.e., name, gender, ID, life or health insurance accounts, etc.). With the enhancement of laws and regulations, the difficulty of personal sensitive data acquisition is increasing. Moreover, the abnormal users called online water army often camouflage themselves to achieve specific goals. They often register false user information such as name, gender, age, etc. To protect privacy and satisfy the needs of camouflage, users and ISPs often hide those sensitive data (i.e., user profile). In this paper, we want to link same user in multiple social networks, which is formally defined as ULASN (User Linkage across Anonymized Social Networks) problem. ULASN is very challenging to address due to (1) the lack of enough ground-truth to build models and obtain accurate prediction results, (2) the studied networks are anonymized, where no user profile or sensitive data is available, and (3) the need of scalable algorithms for user linkage task in large-scale social nateworks, and (4) users in social network are interrelated. To resolve these challenges, a noval user linkage framework based on social structures called ULA is proposed in this paper. ULA tackles these problems by considering massive, low-quality and interrelated user information. It uses few ground-truth to partition users into blocks, which reduces the size of candidates. By extending Fellegi-Sunter methods, our proposed algorithm can handle social network similarity complying to continuous distributions. A probabilistic generative model is proposed and solved by EM algorithm. Simultaneously, missing value problem can also solved when we use EM algorithm to learning parameters. Extensive experiments conducted on two real-world social networks demonstrate that ULA can perform very well in solving ULASN problem.

The purpose of research was to study the existing methods of determining the degree of cohesion of two users of social network, identifying their shortcomings and developing a new method. The research identified shortcomings of existing methods and proposed a new method for assessing the degree of cohesion of social network profiles based on open data from a social network. Under the degree of cohesion of users’ profiles is understood the probability of communication (interaction) of profile owners in real life, it is calculated for two users of the social network and expressed in percent. The work of the method is demonstrated on the example of the social network “In contact”. This method includes the sequence of the following stages: the first stage is data collection about users of the social network with API and the formation of tuples of users’ profile characteristics. A tuple of characteristics of social network profiles is the data, collected for each user, stored in a structured form. The next step is the analysis of the collected information. For each characteristic of the tuple of profiles, i.e. the possible element of interaction of users in the social network, the coefficient of cohesion by the characteristic is calculated. In addition, for each feature, its informativeness is calculated, i.e. how important is this or that feature in this social network. At the final stage, the results are generated, using the formula for the probability of communication between two users, derived during the investigation. Obtained as a result of the application of the method, the probability of communication between two users can be used to optimize the activities of the operative-search services and special bodies. In addition, the received degree of cohesion of two users can be interpreted as the probability of a channel of information leakage between them. The role of the user of the method can be any private or state organization that cares about the security of corporate data and commercial secrets, the operative-search service, as well as an organization that investigates cybercrimes and information security incidents.

The smart grid is developing in the direction of two-way interaction. As an important platform for two-way interaction between the user side and the power system, the social network plays an important role in the development of the smart grid. The structural parameters of the social network include the total number of users and the network density. The network density is reflected by the number of neighbor nodes of the network node and the strength of the connection. Determining the total number of users in the network is beneficial to the smart grid for power allocation. Power system load peaks, insufficient power supply, etc. Deter-mining network density helps speed up the popularization and promotion of smart grid technology and helps to understand the social influence of users in social networks. Offline social network topologies are usually selected by interviewees and then asked to list their neighbors. This paper proposes an inference technique based on so-called “fixed choice” surveys, which requires the interviewee to enumerate all the strong ties and a fixed number of weak ties(e.g.,two or ten). Then use the method of moments to infer the total number of users and the network density of the social network. Experiments using simulated data show that the proposed inference algor-ithm performs well in various network topo-logies and measurement scenarios, and the est-imates obtained are significantly more accu-rate than the estimates obtained directly using the coarse observation network.

The paper is devoted to the problem of assessing the influence of members of social network communities. It proposes a method for constructing a social network model based on a weighted directed graph, considering the intensity of previous interactions between users. The article also considers the possibility of using Walsh orthogonal functions to simulate the diffusion of information influences between users of social networks. The authors conclude that this approach allows for obtaining estimates of the influence of individual users and their coalitions. The paper also evaluates the speed characteristics of the corresponding algorithm and discusses promising areas for its use in ranking social network users by influence, as well as for predicting the results of information influences based on simulation modeling.