Team formation with influence maximization for influential event organization on social networks

Online event-based social services allow users to organize social events by specifying the themes, and invite friends to participate social events. While the event information can be spread over the social network, it is expected that by certain communication between event hosts, users interested in the event themes can be as more as possible. In this paper, by combining the ideas of team formation and influence maximization, we formulate a novel research problem, Influential Team Formation (ITF), to facilitate the organization of social events. Given a set L of required labels to describe the event topics, a social network, and the size k of the host team, ITF is to find a k-node set S that satisfying L and maximizing the Influence-Cost Ratio (i.e., the influence spread per communication cost between team members). Since ITF is proved to be NP-hard, we develop two greedy algorithms and one heuristic method to solve it. Extensive experiments conducted on Facebook and Google+ datasets exhibit the effectiveness and efficiency of the proposed methods. In addition, by employing the real event participation data in Meetup, we show that ITF with the proposed solutions is able to predict organizers of influential events.     

Interplay between trust,information privacy concerns and behavioural intention of users on online social networks

Trust and privacy features of websites have evolved as an important concern for any businesses or interactions, particularly in online networks. The study investigates the relationship between trust, privacy concerns and behavioural intention of users on the social network. The behavioural intention of users on the online social network (OSN) is captured by intention to disclose information and intention to interact with others in OSN. The study was conducted on a sample of 457 active users from one of the major social networking website, Facebook. Partial least squares based structural equation modelling was used for analysing the results. The findings of the study reveal that intention to disclose information mediates the relationship between trust in the website and the intention to interact with others. Another important finding of the study indicates that prior positive experience with the website significantly impacts the trust in website, and the trust in website also plays a crucial role while determining the information privacy concerns in the OSN.     

Recruiting the <Emphasis Type="Italic">K</Emphasis>-most influential prospective workers for crowdsourcing platforms

Viral marketing is widely used by businesses to achieve their marketing objectives using social media. In this work, we propose a customized crowdsourcing approach for viral marketing which aims at efficient marketing based on information propagation through a social network. We term this approach the social community-based crowdsourcing platform and integrate it with an information diffusion model to find the most efficient crowd workers. We propose an intelligent viral marketing framework (IVMF) comprising two modules to achieve this end. The first module identifies the K-most influential users in a given social network for the platform using a novel linear threshold diffusion model. The proposed model considers the different propagation behaviors of the network users in relation to different contexts. Being able to consider multiple topics in the information propagation model as opposed to only one topic makes our model more applicable to a diverse population base. Additionally, the proposed content-based improved greedy (CBIG) algorithm enhances the basic greedy algorithm by decreasing the total amount of computations required in the greedy algorithm (the total influence propagation of a unique node in any step of the greedy algorithm). The highest computational cost of the basic greedy algorithm is incurred on computing the total influence propagation of each node. The results of the experiments reveal that the number of iterations in our CBIG algorithm is much less than the basic greedy algorithm, while the precision in choosing the K influential nodes in a social network is close to the greedy algorithm. The second module of the IVMF framework, the multi-objective integer optimization model, is used to determine which social network should be targeted for viral marketing, taking into account the marketing budget. The overall IVMF framework can be used to select a social network and recruit the K-most influential crowd workers. In this paper, IVMF is exemplified in the domain of personal care industry to show its importance through a real-life case.     

Competition and Information Deception in Online Social Networks

This study empirically examines the role of competition in determining intentions toward personal information deception (PID) among users of online social network (OSN) sites. PID refers to OSN users intentionally misrepresenting or refusing to disclose online personal information. The research proposes that consumers’ intentions toward PID depend on their desire for online competition with other OSN users, which in turn depends on user appraisals of available status and hedonic benefits, as well as established social norms around competition. An analysis of data gathered from 499 OSN participants (students enrolled at a state university in the southeastern United States) shows that competitive desires represent an important antecedent of PID behavior in OSN contexts. Theoretical and practical implications of the research are also discussed.     

Integrated microblog sentiment analysis from users’ social interaction patterns and textual opinions

Traditional post-level opinion classification methods usually fail to capture a person’s overall sentiment orientation toward a topic from his/her microblog posts published for a variety of themes related to that topic. One reason for this is that the sentiments connoted in the textual expressions of microblog posts are often obscure. Moreover, a person’s opinions are often influenced by his/her social network. This study therefore proposes a new method based on integrated information of microblog users’ social interactions and textual opinions to infer the sentiment orientation of a user or the whole group regarding a hot topic. A Social Opinion Graph (SOG) is first constructed as the data model for sentiment analysis of a group of microblog users who share opinions on a topic. This represents their social interactions and opinions. The training phase then uses the SOGs of training sets to construct Sentiment Guiding Matrix (SGM), representing the knowledge about the correlation between users’ sentiments, Textual Sentiment Classifier (TSC), and emotion homophily coefficients of the influence of various types of social interaction on users’ mutual sentiments. All of these support a high-performance social sentiment analysis procedure based on the relaxation labeling scheme. The experimental results show that the proposed method has better sentiment classification accuracy than the textual classification and other integrated classification methods. In addition, IMSA can reduce pre-annotation overheads and the influence from sampling deviation.     

Discerning individual interests and shared interests for social user profiling

Traditionally, research about social user profiling assumes that users share some similar interests with their followees. However, it lacks the studies on what topic and to what extent their interests are similar. Our study in online sharing sites reveals that besides shared interests between followers and followees, users do maintain some individual interests which differ from their followees. Thus, for better social user profiling we need to discern individual interests (capturing the uniqueness of users) and shared interests (capturing the commonality of neighboring users) of the users in the connected world. To achieve this, we extend the matrix factorization model by incorporating both individual and shared interests, and also learn the multi-faceted similarities unsupervisedly. The proposed method can be applied to many applications, such as rating prediction, item level social influence maximization and so on. Experimental results on real-world datasets show that our work can be applied to improve the performance of social rating. Also, it can reveal some interesting findings, such as who likes the “controversial” items most, and who is the most influential in attracting their followers to rate an item.     

Resisting re-identification mining on social graph data

Varieties of sensitive personal information become a privacy concern for social networks. However, characteristics of social graphs could be utilized by attackers to re-identify target entities of social networks. In this paper, we first analyze a new attack model named bin-based attack, which re-identifies social individuals in social networks, according to their graph structure characteristics. For bin-based attack, we propose a novel k-anonymity scheme. With this scheme, social individuals are completely k-anonymity protection. Experiments illustrate the effectiveness of the proposed scheme. The utility of anonymized networks are demonstrated with the results of vertex degree, and betweenness.     

A context-aware approach for trustworthy worker selection in social crowd

Crowdsourcing applications like Amazon Mechanical Turk (AMT) make it possible to address many difficult tasks (e.g., image tagging and sentiment analysis) on the internet and make full use of the wisdom of crowd, where worker quality is one of the most crucial issues for the task owners. Thus, a challenging problem is how to effectively and efficiently select the high quality workers, so that the tasks online can be accomplished successfully under a certain budget. The existing methods on the crowd worker selection problem mainly based on the quality measurement of the crowd workers, those who have to register on the crowdsourcing platforms. With the connect of the OSNs and the crowdsourcing applications, the social contexts like social relationships and social trust between participants and social positions of participants can assist requestors to select one or a group of trustworthy crowdsourcing workers. In this paper, we first present a contextual social network structure and a concept of Strong Social Component (SSC), which emblems a group of workers who have high social contexts values. Then, we propose a novel index for SSC, and a new efficient and effective algorithm C-AWSA to find trustworthy workers, who can complete the tasks with high quality. The results of our experiments conducted on four real OSN datasets illustrate that the superiority of our method in trustworthy worker selection.     

On route planning by inferring visiting time,modeling user preferences,and mining representative trip patterns

Location-based services allow users to perform check-in actions, which record the geo-spatial activities and provide a plentiful source to do more accurate and useful geographical recommendation. In this paper, we present a novel Preferred Time-aware Route Planning (PTRP) problem, which aims to recommend routes whose locations are not only representative but also need to satisfy users’ preference. The central idea is that the goodness of visiting locations along a route is significantly affected by the visiting time and user preference, and each location has its own proper visiting time due to its category and population. We develop a four-stage preference-based time-aware route planning framework. First, since there is usually either noise time on existing locations or no visiting information on new locations, we devise an inference method, LocTimeInf, to predict the location visiting time on routes. Second, considering the geographical, social, and temporal information of users, we propose the GST-Clus method to group users with similar location visiting preferences. Third, we find the representative and popular time-aware location-transition behaviors by proposing Time-aware Transit Pattern Mining (TTPM) algorithm. Finally, based on the mined time-aware transit patterns, we develop a Preferred Route Search (PR-Search) algorithm to construct the final time-aware routes. Experiments on Gowalla and Foursquare check-in data exhibit the promising effectiveness and efficiency of the proposed methods, comparing to a series of competitors.     

Bottleneck-aware arrangement over event-based social networks: the max-min approach

With the popularity of mobile computing and social media, various kinds of online event-based social network (EBSN) platforms, such as Meetup, Plancast and Whova, are gaining in prominence. A fundamental task of managing EBSN platforms is to recommend suitable social events to potential users according to the following three factors: spatial locations of events and users, attribute similarities between events and users, and friend relationships among users. However, none of the existing approaches considers all the aforementioned influential factors when they recommend users to proper events. Furthermore, the existing recommendation strategies neglect the bottleneck cases of the global recommendation. Thus, it is impossible for the existing recommendation solutions to be fair in real-world scenarios. In this paper, we first formally define the problem of bottleneck-aware social event arrangement (BSEA), which is proven to be NP-hard. To solve the BSEA problem approximately, we devise two greedy heuristic algorithms, Greedy and Random+Greedy, and a local-search-based optimization technique. In particular, the Greedy algorithm is more effective but less efficient than the Random+Greedy algorithm in most cases. Moreover, a variant of the BSEA problem, called the Extended BSEA problem, is studied, and the above solutions can be extended to address this variant easily. Finally, we conduct extensive experiments on real and synthetic datasets which verify the efficiency and effectiveness of our proposed algorithms.     

Forming an intention to act on recommendations given via online social networks

Online social networks (OSN) are technology-enabled relationship tools in which a user creates a profile, connects to other individuals, and shares user-generated content with them. One of the many features OSN offer is the ability to post messages in the form of product and service recommendations. Although word-of-mouth research has examined this type of personal communication to intentionally influence consumer decisions, limited understanding exists regarding when a user acts upon a recommendation received from one of their contacts. In this study, we draw on relationship theories and research on trust to analyze the impact relationship characteristics and behaviors of the OSN contacts have on trust in the recommendation that subsequently leads a user to act on this OSN contact’s recommendation. The results of a survey of 116 OSN users showed that closeness, excessive posting behavior, and past recommendation experience have a positive impact on trust and intention to act on the recommendation. One characteristic of the relationships, that is, similarity, however, does not affect recommendation trust in the context of OSN. The findings enhance our understanding of relationships and their defining characteristics in OSN. The results also provide insights into how companies might leverage OSN in their marketing efforts.     

CP2: Cryptographic privacy protection framework for online social networks

In Online Social Networks (OSNs), users interact with each other by sharing their personal information. One of the concerns in OSNs is how user privacy is protected since the OSN providers have full control over users’ data. The OSN providers typically store users’ information permanently; the privacy controls embedded in OSNs offer few options to users for customizing and managing the dissipation of their data over the network. In this paper, we propose an efficient privacy protection framework for OSNs that can be used to protect the privacy of users’ data and their online social relationships from third parties. The recommended framework shifts the control over data sharing back to the users by providing them with flexible and dynamic access policies. We employ a public-key broadcast encryption scheme as the cryptographic tool for managing information sharing with a subset of a user’s friends. The privacy and complexity evaluations show the superiority of our approach over previous.     

A linear-time algorithm for finding Hamiltonian (<Emphasis Type="Italic">s</Emphasis>, <Emphasis Type="Italic">t</Emphasis>)-paths in odd-sized rectangular grid graphs with a rectangular hole

A grid graph \(G_{\mathrm{g}}\) is a finite vertex-induced subgraph of the two-dimensional integer grid \(G^\infty \). A rectangular grid graph R(m, n) is a grid graph with horizontal size m and vertical size n. A rectangular grid graph with a rectangular hole is a rectangular grid graph R(m, n) such that a rectangular grid subgraph R(k, l) is removed from it. The Hamiltonian path problem for general grid graphs is NP-complete. In this paper, we give necessary conditions for the existence of a Hamiltonian path between two given vertices in an odd-sized rectangular grid graph with a rectangular hole. In addition, we show that how such paths can be computed in linear time.     

Community detection in social networks using user frequent pattern mining

Recently, social networking sites are offering a rich resource of heterogeneous data. The analysis of such data can lead to the discovery of unknown information and relations in these networks. The detection of communities including ‘similar’ nodes is a challenging topic in the analysis of social network data, and it has been widely studied in the social networking community in the context of underlying graph structure. Online social networks, in addition to having graph structures, include effective user information within networks. Using this information leads to enhance quality of community discovery. In this study, a method of community discovery is provided. Besides communication among nodes to improve the quality of the discovered communities, content information is used as well. This is a new approach based on frequent patterns and the actions of users on networks, particularly social networking sites where users carry out their preferred activities. The main contributions of proposed method are twofold: First, based on the interests and activities of users on networks, some small communities of similar users are discovered, and then by using social relations, the discovered communities are extended. The F-measure is used to evaluate the results of two real-world datasets (Blogcatalog and Flickr), demonstrating that the proposed method principals to improve the community detection quality.     

Mathematical models and routing algorithms for CAD technological preparation of cutting processes

Resource-conscious technologies for cutting sheet material include the ICP and ECP technologies that allow for aligning fragments of the contours of cutouts. In this work, we show the mathematical model for the problem of cutting out parts with these technologies and algorithms for finding cutting tool routes that satisfy technological constraints. We give a solution for the problem of representing a cutting plan as a plane graph G = (V,F,E), which is a homeomorphic image of the cutting plan. This has let us formalize technological constraints on the trajectory of cutting the parts according to the cutting plan and propose a series of algorithms for constructing a route in the graph G = (V,F,E), which is an image of an admissible trajectory. Using known coordinates of the preimages of vertices of graph G = (V,F,E) and the locations of fragments of the cutting plan that are preimages of edges of graph G = (V,F,E), the resulting route in the graph G = (V,E) can be interpreted as the cutting tool’s trajectory.The proposed algorithms for finding routes in a connected graph G have polynomial computational complexity. To find the optimal route in an unconnected graph G, we need to solve, for every dividing face f of graph G, a travelling salesman problem on the set of faces incident to f.     

The pursuit of trust in <Emphasis Type="Italic">ad hoc</Emphasis> virtual teams: how much electronic portrayal is too much?

This study develops and tests the concept of electronic portrayal in synchronous computer-mediated communication of ad hoc virtual teams. Electronic portrayal is the extent to which a communication system portrays the true identity of its users. A theoretical model is developed based upon which it is hypothesized that increased information available due to electronic portrayal will impact trust in ad hoc virtual teams. An experiment is conducted to test the model by manipulating the graphical identification of users of a system as well as the rehearsability of the system. Rehearsability is the extent to which users can reread and edit their messages before submitting them to the synchronous communication system. The results show that the combination of both factors – identification and rehearsability – impacts trust among team members. Specifically, partial electronic portrayal (only one form of true-to-life representation) has the most positive impact on trust. This effect is moderated by communication-related variables such as self-disclosure, impressions and virtual co-presence. The implication of these results is that too much true identity information negatively impacts trust. This research provides theoretical and practical contributions for understanding the importance of identification and rehearsability in synchronous group communication.     

Inferring user interests in microblogging social networks: a survey

With the growing popularity of microblogging services such as Twitter in recent years, an increasing number of users are using these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications and areas. Inferring user interests plays a significant role in providing personalized recommendations on microblogging services, and also on third-party applications providing social logins via these services, especially in cold-start situations. In this survey, we review user modeling strategies with respect to inferring user interests from previous studies. To this end, we focus on four dimensions of inferring user interest profiles: (1) data collection, (2) representation of user interest profiles, (3) construction and enhancement of user interest profiles, and (4) the evaluation of the constructed profiles. Through this survey, we aim to provide an overview of state-of-the-art user modeling strategies for inferring user interest profiles on microblogging social networks with respect to the four dimensions. For each dimension, we review and summarize previous studies based on specified criteria. Finally, we discuss some challenges and opportunities for future work in this research domain.     

Recommendations based on a heterogeneous spatio-temporal social network

Recommender systems in location-based social networks (LBSNs), such as Facebook Places and Foursquare, have focused on recommending friends or locations to registered users by combining information derived from explicit (i.e. friendship network) and implicit (i.e. user-item rating network, user-location network, etc.) sub-networks. However, previous models were static and failed to adequately capture user time-varying preferences. In this paper, we provide a novel recommendation method based on the time dimension as well. We construct a hybrid tripartite (i.e., user, location, session) graph, which incorporates 7 different unipartite and bipartite graphs. Then, we test it with an extended version of the Random Walk with Restart (RWR) algorithm, which randomly walks through the network by using paths of 7 differently weighted edge types (i.e., user-location, user-session, user-user, etc.). We evaluate experimentally our method and compare it against three state-of-the-art algorithms on two real-life datasets; we show a significant prevalence of our method over its competitors.     

Mining coterie patterns from Instagram photo trajectories for recommending popular travel routes

Instagram is a popular photo-sharing social application. It is widely used by tourists to record their journey information such as location, time and interest. Consequently, a huge volume of geo-tagged photos with spatio-temporal information are generated along tourist’s travel trajectories. Such Instagram photo trajectories consist of travel paths, travel density distributions, and traveller behaviors, preferences, and mobility patterns. Mining Instagram photo trajectories is thus very useful for many mobile and location-based social applications, including tour guide and recommender systems. However, we have not found any work that extracts interesting group-like travel trajectories from Instagram photos asynchronously taken by different tourists. Motivated by this, we propose a novel concept: coterie, which reveals representative travel trajectory patterns hidden in Instagram photos taken by users at shared locations and paths. Our work includes the discovery of (1) coteries, (2) closed coteries, and (3) the recommendation of popular travel routes based on closed coteries. For this, we first build a statistically reliable trajectory database from Instagram geo-tagged photos. These trajectories are then clustered by the DBSCAN method to find tourist density. Next, we transform each raw spatio-temporal trajectory into a sequence of clusters. All discriminative closed coteries are further identified by a Cluster-Growth algorithm. Finally, distance-aware and conformityaware recommendation strategies are applied on closed coteries to recommend popular tour routes. Visualized demos and extensive experimental results demonstrate the effectiveness and efficiency of our methods.     

Incremental <Emphasis Type="Italic">k</Emphasis>-core decomposition: algorithms and evaluation

A k-core of a graph is a maximal connected subgraph in which every vertex is connected to at least k vertices in the subgraph. k-core decomposition is often used in large-scale network analysis, such as community detection, protein function prediction, visualization, and solving NP-hard problems on real networks efficiently, like maximal clique finding. In many real-world applications, networks change over time. As a result, it is essential to develop efficient incremental algorithms for dynamic graph data. In this paper, we propose a suite of incremental k-core decomposition algorithms for dynamic graph data. These algorithms locate a small subgraph that is guaranteed to contain the list of vertices whose maximum k-core values have changed and efficiently process this subgraph to update the k-core decomposition. We present incremental algorithms for both insertion and deletion operations, and propose auxiliary vertex state maintenance techniques that can further accelerate these operations. Our results show a significant reduction in runtime compared to non-incremental alternatives. We illustrate the efficiency of our algorithms on different types of real and synthetic graphs, at varying scales. For a graph of 16 million vertices, we observe relative throughputs reaching a million times, relative to the non-incremental algorithms.