Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests

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.

The behaviour of individual users in an online social network is a major contributing factor in determining the outcome of multiple network phenomenon. Group formation, growth of the network, information propagation, and rumour blocking are some of the many network behavioural traits that are influenced by the interaction patterns of the users in the network. Network motifs capture one such interaction pattern between users in online social networks (OSNs). For this work, four second-order (two-edged) network motifs have been considered, namely, message receiving pattern, message broadcasting pattern, message passing pattern, and reciprocal message pattern, to analyse user behaviour in online social networks. This work provides and utilizes a node interaction pattern-finding algorithm to identify the frequency of aforementioned second-order network motifs in six real-life online social networks (Facebook, GPlus, GNU, Twitter, Enron Email, and Wiki-vote). The frequency of network motifs participated in by a node is considered for the relative ranking of all nodes in the online social networks. The highest-rated nodes are considered seeds for information propagation. The performance of using network motifs for ranking nodes as seeds for information propagation is validated using statistical metrics Z-score, concentration, and significance profile and compared with baseline ranking methods in-degree centrality, out-degree centrality, closeness centrality, and PageRank. The comparative study shows the performance of centrality measures to be similar or better than second-order network motifs as seed nodes in information diffusion. The experimental results on finding frequencies and importance of different interaction patterns provide insights on the significance and representation of each such interaction pattern and how it varies from network to network.

Analysis of opinion dynamics in social networks plays an important role in today's life. For applications such as predicting users' political preference, it is particularly important to be able to analyze the dynamics of competing opinions. While observing the evolution of polar opinions of a social network's users over time, can we tell when the network "behaved" abnormally? Furthermore, can we predict how the opinions of the users will change in the future? Do opinions evolve according to existing network opinion dynamics models? To answer such questions, it is not sufficient to study individual user behavior, since opinions can spread far beyond users' egonets. We need a method to analyze opinion dynamics of all network users simultaneously and capture the effect of individuals' behavior on the global evolution pattern of the social network. In this work, we introduce Social Network Distance (SND) - a distance measure that quantifies the "cost" of evolution of one snapshot of a social network into another snapshot under various models of polar opinion propagation. SND has a rich semantics of a transportation problem, yet, is computable in time linear in the number of users, which makes SND applicable to the analysis of large-scale online social networks. In our experiments with synthetic and real-world Twitter data, we demonstrate the utility of our distance measure for anomalous event detection. It achieves a true positive rate of 0.83, twice as high as that of alternatives. When employed for opinion prediction in Twitter, our method's accuracy is 75.63%, which is 7.5% higher than that of the next best method. Source Code: https://cs.ucsb.edu/~victor/pub/ucsb/dbl/snd/

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 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 daily exposure of social media users to propaganda and disinformation campaigns has reinvigorated the need to investigate the local and global patterns of diffusion of different (mis)information content on social media. Echo chambers and influencers are often deemed responsible of both the polarization of users in online social networks and the success of propaganda and disinformation campaigns. This article adopts a data-driven approach to investigate the structuration of communities and propaganda networks on Twitter in order to assess the correctness of these imputations. In particular, the work aims at characterizing networks of propaganda extracted from a Twitter dataset by combining the information gained by three different classification approaches, focused respectively on (i) using Tweets content to infer the “polarization” of users around a specific topic, (ii) identifying users having an active role in the diffusion of different propaganda and disinformation items, and (iii) analyzing social ties to identify topological clusters and users playing a “central” role in the network. The work identifies highly partisan community structures along political alignments; furthermore, centrality metrics proved to be very informative to detect the most active users in the network and to distinguish users playing different roles; finally, polarization and clustering structure of the retweet graphs provided useful insights about relevant properties of users exposure, interactions, and participation to different propaganda items.

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.

Purpose As social networking services are popular, the need for recognizing reliable people has become the main concern. Quantifying the relationships between the users of social networks is also one of the important challenges with the increasing number of users. So, trust plays an important role in social networks in order to recognize trustworthy people. The purpose of this paper is to propose an approach to recognize trustworthy users and protect users from abuse by untrustable users. Design/methodology/approach In this paper, the authors suggest a method to measure the trust value between users of social networks based on call log histories such as abundance, novelty, and sincerity and quality of service (QoS) requirements such as accessibility, response ability, tend to respond, and agility. After that, the authors calculate error-hit, precision, and recall to evaluate the trust value. Findings The results indicate that the proposed approach has better performance in terms of error-hit, precision, and recall than FIFO, combined, QoS-based, and call log-based method. Originality/value In this paper, the trust issue in social networks is pointed out and a new approach to evaluate the trust value is proposed based on call log histories and QoS requirements.

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

Sentiment analysis research has focused on using text for predicting sentiments without considering the unavoidable peer influence on user emotions and opinions. The lack of large-scale ground-truth data on sentiments of users in social networks has limited research on how predictable sentiments are from social ties. In this paper, using a large-scale dataset on human sentiments, we study sentiment prediction within social networks. We demonstrate that sentiments are predictable using structural properties of social networks alone. With social science and psychology literature, we provide evidence on sentiments being connected to social relationships at four different network levels, starting from the ego-network level and moving up to the whole-network level. We discuss emotional signals that can be captured at each level of social relationships and investigate the importance of structural features on each network levels. We demonstrate that sentiment prediction that solely relies on social network structure can be as (or more) accurate than text-based techniques. For the situations where complete posts and friendship information are difficult to get, we analyze the trade-off between the sentiment prediction performance and the available information. When computational resources are limited, we show that using only four network properties, one can predict sentiments with competitive accuracy. Our findings can be used to (1) validate the peer influence on user sentiments, (2) improve classical text-based sentiment prediction methods, (3) enhance friend recommendation by utilizing sentiments, and (4) help identify personality traits.

Using multimodalities, the field of biometric is receiving widespread acceptance for efficient communication between users in social network. Hence, suitable designing of authentication and access control is highly required to perform analysis of system in social network domain. In this work, a Proximal Spatial Vector and Affinity Coefficient (PSV-AC) framework is designed with the objective of ensuring authentic communication between users in social network using face and fingerprint. Initially, multimodal user biometric features with proximal feature pixel stored in spatial vector are gene encoded to obtain biometric identity keys. User authenticity is authorized by decoding gene encoded biometric features using biometric identity keys. User Affinity Coefficient is calculated via antecedent instances shared between different social users. The social network user’s affinity coefficient is ordered in a matrix. Matrix ranking is then performed to identify whether authentic user social communication happened in network. Experiments have been carried out using BioSecure datasets to measure the performance in terms of number of social users, social network authentication time, access control time, multimodal biometric feature template size, and true positive rate.

The present study aimed to detect the levels of awareness of citizenship among the users of social networks of Al-Aqsa University students and its relation to some variables (the used network, gender, skill and knowledge of the Internet, accept dealing with the Internet). In order to achieve the objectives of the study, the researcher used the descriptive approach. He designed the scale of awareness of citizenship to include four fields: ethics, culture, critical protection and effective participation in the Internet. After applying the study to a sample of (700) students of AlAqsa University, the results deduced that: Generally the level of awareness of citizenship reached (76.08%), which means above the average. Focusing on the terms of the scale dimensions, the averages were low represented by actual participation, where the percentage was 60.38%, and medium represented by digital culture and critical protection, where percentages were (72.18% and 79.57%) respectively, while the percentage of the awareness of the ethics of citizenship appeared at the highest level, where the percentage of this dimension was (88.29%). There is also a difference in the level of the awareness of the indicators of citizenship in some dimensions of the users of social networks, depending on: the used social network, gender, level of skill and knowledge of the Internet, and level of acceptance of dealing with the Internet.

Computational trust in the social media depends both on the interaction types and on the expertise, which exposes user’s interests by tags on items such as books, articles, images etc. However, when there is not any interaction among users, such a computation is impossible. In this paper, we first propose a novel model of computational trust among users in social network to incorporate the interaction among users and semantics of topics based on tags posted by users. Both types of direct and indirect interaction via intermediate ones are utilized for computation in the model. Then, we introduce algorithms for computing trust among users in social network.

Information diffusion over social networks becomes a hot topic recently. Most of the existing works are based on the machine learning method with social network structure analysis and empirical data mining. However, the results learned from some specific dataset may not apply to the future networks, since the social network structure is in a highly dynamic environment. Moreover, the dynamics of information diffusion are also heavily influenced by network users’ decisions, actions and their socio-economic interactions, which is generally ignored by existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks, which focuses on the users’ behavior analysis from a microeconomics points of view. We also conduct experiments by using real-world Twitter information diffusion dataset, which shows that the proposed evolutionary game theoretic model is effective and practical in modeling the social network users’ information diffusion dynamics.