Heterogeneous Information Network based Adaptive Social Influence Learning for Recommendation and Explanation

Social networks provide customs with a platform for interaction and information sharing. In real social activities, whether individuals or businesses, have to rely on some relations to live, work or engage in commercial activities. The formation of relationship between different actor clusters based on the same actor in real social networks is described in the paper. The relationships and their types as well as relationship attributes in real complex social networks are analyzed in details. An index calledQinshuduto represent the degree of closeness between two actors in real complex social networks is proposed and its computation model based on relationship types and attributes etc. are given. Case study shows thatQinshuduis a very reasonable and effective way for the use of complex social networks.

Each social network can be modeled as a graph G = [V, E] in which V is a vertex representing a person in this social network, and E is an edge representing the existence of a relationship between two individuals. The social infrastructure with size m is known as the Km group in the social network G with m individual. In other words, in a Km, each person knows other individuals and is in touch with them. The present study aims at developing a method for optimizing interpersonal communication in the social network using a simple cellular automaton algorithm. The experimental results obtained from both simulated social network and two real social networks were analyzed. The findings revealed that the proposed method has the potential to considerably reduce not only the number of colors assigned but also the running time of the program.

Various networks exist in the world today including biological, social, information, and communication networks with the Internet as the largest network of all. One salient structural feature of these networks is the formation of groups or communities of vertices that tend to be more connected to each other within the same group than to those outside. Therefore, the detection of these communities is a topic of great interest and importance in many applications and different algorithms including label propagation have been developed for such purpose. Speaker-listener label propagation algorithm (SLPA) enjoys almost linear time complexity, so desirable in dealing with large networks. As an extension of SLPA, this study presented a novel weighted label propagation algorithm (WLPA), which was tested on four real world social networks with known community structures including the famous Zachary's karate club network. Wilcoxon tests on the communities found in the karate club network by WLPA demonstrated an improved statistical significance over SLPA. Withthehelp of Wilcoxon tests again, we were able to determine the best possible formation of two communities in this network relative to the ground truth partition, which could be used as a new benchmark for assessing community detection algorithms. Finally WLPA predicted better communities than SLPA in two of the three additional real social networks, when compared to the ground truth.

Collaborative filtering has been very successful in both research and E-commence applications. One of the most popular collaborative filtering algorithms is the k-Nearest Neighbor (KNN) method, which finds k nearest neighbors for a given user to predict his interests. Previous research on KNN algorithm usually suffers from the data sparseness problem, because the quantity of items users voted is really small. The problem is more severe in web-based applications. Cluster-based collaborative filtering has been proposed to solve the sparseness problem by averaging the opinions of the similar users. However, it does not bring consistent improvement on the performance of collaborative filtering since it produces less-personal prediction. In this paper, we propose a clustering-based KNN method, which combines the iterative clustering algorithm and the KNN to improve the performance of collaborative filtering. Using the iterative clustering approach, the sparseness problem could be solved by fully exploiting the voting information first. Then, as a smoothing method to the KNN method, cluster-based KNN is used to optimize the performance of collaborative filtering. The experimental results show that our proposed cluster-based KNN method can perform consistently better than the traditional KNN method and clustering-based method in large-scale data sets.

We live in a "small world," where two arbitrary people are likely connected by a short chain of intermediate friends. With scant information about a target individual, people can successively forward a message along such a chain. Experimental studies have verified this property in real social networks, and theoretical models have been advanced to explain it. However, existing theoretical models have not been shown to capture behavior in real-world social networks. Here, we introduce a richer model relating geography and social-network friendship, in which the probability of befriending a particular person is inversely proportional to the number of closer people. In a large social network, we show that one-third of the friendships are independent of geography and the remainder exhibit the proposed relationship. Further, we prove analytically that short chains can be discovered in every network exhibiting the relationship.

Real social networks usually have some structural features of the complex networks, such as community structure, the scale-free degree distribution, clustering, small world network, dynamic evolution and so on. A new community detecting algorithm for directed and weighted social networks is proposed in this paper. Due to the use of more reference information, the accuracy of the algorithm is better than some of the typical detecting algorithms. And because of the use of heap structure and multi-task modular architecture, the algorithm also got a high computational efficiency than other algorithms. The effectiveness and efficiency of the algorithm is validated by experiments on real social networks. Based on the theories and models of complex networks, the features of the real large social networks are analyzed.

Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the “New Network Community Detection” problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively.

Identifying the most influential individuals can provide invaluable help in developing and deploying effective viral marketing strategies. Previous studies mainly focus on designing efficient algorithms or heuristics to find top-K influential nodes on a given static social network. While, as a matter of fact, real-world social networks keep evolving over time and a recalculation upon the changed network inevitably leads to a long running time, significantly affecting the efficiency. In this paper, we observe from real-world traces that the evolution of social network follows the preferential attachment rule and the influential nodes are mainly selected from high-degree nodes. Such observations shed light on the design of IncInf, an incremental approach that can efficiently locate the top-K influential individuals in evolving social networks based on previous information instead of calculation from scratch. In particular, IncInf quantitatively analyzes the influence spread changes of nodes by localizing the impact of topology evolution to only local regions, and a pruning strategy is further proposed to effectively narrow the search space into nodes experiencing major increases or with high degrees. We carried out extensive experiments on real-world dynamic social networks including Facebook, NetHEPT, and Flickr. Experimental results demonstrate that, compared with the state-of-the-art static heuristic, IncInf achieves as much as 21\(\times \) speedup in execution time while maintaining matching performance in terms of influence spread.

Social Networks have experienced increased popularity and rapid growth in recent years. Recommendation is significant for users due to the extremely large amount of information in Social Networks. Most existing recommender systems rely on collaborative filtering techniques which focus on recommending the most relevant items to users based on past rating information of users or items. In Social Networks, the cold-start and data sparsity problems are very serious because new users and items are growing rapidly. Taking the Event Recommendation problem in Event-Based Social Networks as a scenario, many events are newly created and have few feedbacks. Existed collaborative filtering based methods will fail for Social Recommendation due to these problems. Therefore, a more sophisticated recommendation mechanism that can efficiently combine various contextual information to further improve recommendation quality is desired. In this paper, we propose a Context Aware Matrix Factorization model called CAMF which models implicit feedbacks and various contextual information simultaneously for Social Recommendation. Specifically, CAMF is a unified model that combines the Matrix Factorization model which models implicit feedbacks with the Linear Contextual Features model which models explicit contextual features. Extensive experiments on a large real-world dataset demonstrate that the CAMF model significantly outperforms state-of-the-art methods by 12.7% in terms of accuracy for the Event Recommendation problem.

Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods. However, most existing models assume that social effects from friend users are static and under the forms of constant weights or fixed constraints. To relax this strong assumption, in this paper, we propose dual graph attention networks to collaboratively learn representations for two-fold social effects, where one is modeled by a user-specific attention weight and the other is modeled by a dynamic and context-aware attention weight. We also extend the social effects in user domain to item domain, so that information from related items can be leveraged to further alleviate the data sparsity problem. Furthermore, considering that different social effects in two domains could interact with each other and jointly influence user preferences for items, we propose a new policy-based fusion strategy based on contextual multi-armed bandit to weigh interactions of various social effects. Experiments on one benchmark dataset and a commercial dataset verify the efficacy of the key components in our model. The results show that our model achieves great improvement for recommendation accuracy compared with other state-of-the-art social recommendation methods.

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

At present, the traditional collaborative filtering (CF) recommendation algorithm generally has the problems of data sparsity and low accuracy. In order to solving these problems, this paper proposes a multi-feature fusion collaborative filtering recommendation algorithm. Firstly, considering that different users have different preferences for different item attributes, this paper used item attribute scores and user preference information for matrix filling to alleviate the problem of data sparsity. Secondly, considering the influence of multiple features of user activity , the count of common item ratings, and item popularity on the calculation of user similarity, and used these features to improve the calculation of similarity. Finally, the matrix after data filling and the improved similarity calculation formula were used to make personalized recommendations for users. Experimental results prove that compared with traditional algorithms and other improved algorithms, the improved algorithm in this paper alleviates the problem of data sparsity to a certain extent, and at the same time has a certain improvement in accuracy

A heterogeneous information network (HIN) is one whose nodes model objects of different types and whose links model objects' relationships. In many applications, such as social networks and RDF-based knowledge bases, information can be modeled as HINs. To enrich its information content, objects (as represented by nodes) in an HIN are typically associated with additional attributes. We call such an HIN an Attributed HIN or AHIN. We study the problem of clustering objects in an AHIN, taking into account objects' similarities with respect to both object attribute values and their structural connectedness in the network. We show how supervision signal, expressed in the form of a must-link set and a cannot-link set, can be leveraged to improve clustering results. We put forward the SCHAIN algorithm to solve the clustering problem. We conduct extensive experiments comparing SCHAIN with other state-of-the-art clustering algorithms and show that SCHAIN outperforms the others in clustering quality.

This paper reports on results obtained by deploying HEALER and DOSIM (two AI agents for social influence maximization) in the real-world, which assist service providers in maximizing HIV awareness in real-world homeless-youth social networks. These agents recommend key "seed" nodes in social networks, i.e., homeless youth who would maximize HIV awareness in their real-world social network. While prior research on these agents published promising simulation results from the lab, the usability of these AI agents in the real-world was unknown. This paper presents results from three real-world pilot studies involving 173 homeless youth across two different homeless shelters in Los Angeles. The results from these pilot studies illustrate that HEALER and DOSIM outperform the current modus operandi of service providers by ~160% in terms of information spread about HIV among homeless youth.

KCRec: Knowledge-aware representation Graph Convolutional Network for Recommendation