BibClus: A Clustering Algorithm of Bibliographic Networks by Message Passing on Center Linkage Structure

Extracting a core structure from heterogeneous information network using h-subnet and meta-path strength

In this paper, we explore heterogenous information networks in which each vertex represents one entity and the edges reflect linkage relationships. Heterogenous information networks contain vertices of several entity types, such as papers, authors and terms, and hence can fully reflect multiple linkage relationships among different entities. Such a heterogeneous information network is similar to a mixed media graph (MMG). By representing a bibliographic dataset as an MMG, the performance obtained when searching relevant entities (e.g., papers) can be improved. Furthermore, our academic search enables multiple-entity search, where a variety of entity search results are provided, such as relevant papers, authors and conferences, via a one-time query. Explicitly, given a bibliographic dataset, we propose a Global-MMG, in which a global heterogeneous information network is built. When a user submits a query keyword, we perform a random walk with restart (RWR) to retrieve papers or other types of entity objects. To reduce the query response time, algorithm Net-MMG (standing for NetClus-based MMG) is developed. Algorithm Net-MMG first divides a heterogeneous information network into a collection of sub-networks. Afterward, the Net-MMG performs a RWR on a set of selected relevant sub-networks. We implemented our academic search and conducted extensive experiments using the ACM Digital Library. The experimental results show that by exploring heterogeneous information networks and RWR, both the Global-MMG and Net-MMG achieve better search quality compared with existing academic search services. In addition, the Net-MMG has a shorter query response time while still guaranteeing good quality in search results.

Information network derived from various domains are studied recently. Searching for Similarity is a major task into such types Information Network. Lot of research on computing similar objects is done in Homogeneous Information Network. But real world scenario can be described easily by Heterogeneous Information Network (HIN) which consists of different types of entities and relationship among them. Due to multiple type of entities and links between them in HIN, it is necessary to find the similarities between the nodes of HIN. In Homogeneous Information Network, there is only single type of node and links in between them. There are many existing methods by which similarity among the nodes of Homogeneous Information Network can be calculated. But those methods cannot be applied for the HIN because semantic meaning behind each path cannot be considered. If we want to apply techniques of Homogeneous Information Network on HIN then we need to project HIN into Homogeneous Information Network which causes loss of Information. So there is a need to apply different techniques or similarity measures on HIN to calculate the similarities between nodes in HIN. There are many similarity measures implemented by researchers for HIN. Similarity search basically concentrates on discovering the most similarity objects for a given query entity. In a comparative analysis section, we have discussed some of the measures used for similarity.

Recently, heterogeneous information network (HIN) analysis has attracted a lot of attention, and many data mining tasks have been exploited on HIN. As an important data mining task, recommender system includes a lot of object types (e.g., users, movies, actors, and interest groups in movie recommendation) and the rich relations among object types, which naturally constitute an HIN. The comprehensive information integration and rich semantic information of HIN make it promising to generate better recommendation. In this chapter, we introduce three works on recommendation with HIN. One work recommends items with semantic meta paths, and the other two works extend traditional matrix factorization with rich heterogeneous information.

Many complex systems derived from nature and society consist of multiple types of entities and heterogeneous interactions, which can be effectively modeled as heterogeneous information network (HIN). Structural analysis of heterogeneous networks is of great significance by leveraging the rich semantic information of objects and links in the heterogeneous networks. And, clustering heterogeneous networks aims to group vertices into classes, which sheds light on revealing the structure–function relations of the underlying systems. The current algorithms independently perform the feature extraction and clustering, which are criticized for not fully characterizing the structure of clusters. In this study, we propose a learning model by joint <underline>G</underline>raph <underline>E</underline>mbedding and <underline>N</underline>onnegative <underline>M</underline>atrix <underline>F</underline>actorization (aka GEjNMF ), where feature extraction and clustering are simultaneously learned by exploiting the graph embedding and latent structure of networks. We formulate the objective function of GEjNMF and transform the heterogeneous network clustering problem into a constrained optimization problem, which is effectively solved by l 0 -norm optimization. The advantage of GEjNMF is that features are selected under the guidance of clustering, which improves the performance and saves the running time of algorithms at the same time. The experimental results on three benchmark heterogeneous networks demonstrate that GEjNMF achieves the best performance with the least running time compared with the best state-of-the-art methods. Furthermore, the proposed algorithm is robust across heterogeneous networks from various fields. The proposed model and method provide an effective alternative for heterogeneous network clustering.

Heterogeneous network user authentication keys are vulnerable to plain text attacks, resulting in poor encryption performance. Therefore, a heterogeneous bilinear pairing signcryption scheme is proposed. The symbol frequency detection method is used to detect symbols of user authentication data, establish an ORK user protocol in a heterogeneous network, and perform a pairwise signcryption scheme to linearly encode user cognitive information in a heterogeneous network. Use Ryption technology to authenticate and re-sign users in heterogeneous networks, and use a bilinear pairing signature encryption scheme to control cipher text in user processes to encrypt identity authentication information in heterogeneous networks. Establish a statistical analysis model to encrypt user authentication information in a heterogeneous network to achieve intelligence. Simulation results show that the method has good scrambling ability and strong anti-attack ability. It is used for bilinear pairing signature encryption of user authentication keys in heterogeneous networks.

Contextual bandit serves as an invaluable tool to balance theexploration vs. exploitationtradeoff in various applications such as online recommendation. In many applications,heterogeneous information networks(HINs) provide rich side information for contextual bandits, such as different types of attributes and relationships among users and items. In this article, we propose the first HIN-assisted contextual bandit framework, which utilizes a given HIN to assist contextual bandit learning. The proposed framework uses meta-paths in HIN to extract rich relations among users and items for the contextual bandit. The main challenge is how to leverage these relations, since users’ preference over items, the target of our online learning, are closely related to users’ preference over meta-paths. However, it is unknown which meta-path a user prefers more. Thus, both preferences are needed to be learned in an online fashion with exploration vs. exploitation tradeoff balanced. We propose the HIN-assisted upper confidence bound (HUCB) algorithm to address such a challenge. For each meta-path, the HUCB algorithm employs an independent base bandit algorithm to handle online item recommendations by leveraging the relationship captured in this meta-path. A bandit master is then employed to learn users’ preference over meta-paths to dynamically combine base bandit algorithms with a balance of exploration vs. exploitation tradeoff. We theoretically prove that the HUCB algorithm can achieve similar performance compared with the optimal algorithm where each user is served according to his true preference over meta-paths (assuming the optimal algorithm knows the preference). Moreover, we prove that the HUCB algorithm benefits from leveraging HIN in achieving a smaller regret upper bound than the baseline algorithm without leveraging HIN. Experimental results on a synthetic dataset, as well as real datasets from LastFM and Yelp demonstrate the fast learning speed of the HUCB algorithm.

Twitter, a microblogging platform, has developed into an increasingly invaluable information source, where millions of users post a great quantity of tweets with various topics per day. Heterogeneous information networks consisting of multi-type objects and relations are becoming more and more prevalent as an organization form of knowledge and information. The task of linking an entity mention in a tweet with its corresponding entity in a heterogeneous information network is of great importance, for the purpose of enriching heterogeneous information networks with the abundant and fresh knowledge embedded in tweets. However, the entity mention is ambiguous. Additionally, tweets are short and informal, making it difficult to mine enough information from a single tweet for entity linking. In this paper, we propose an unsupervised iterative clustering framework TELHIN to link multiple similar tweets with a heterogeneous information network jointly. Our framework takes three dimensions of tweet similarity into consideration: (1) content similarity, (2) temporal similarity, and (3) user similarity. The appropriate weights of different similarity dimensions for each entity mention are learned iteratively based on the metric learning algorithm by leveraging the pairwise constraints generated automatically. Experiments on real data demonstrate the effectiveness of our framework in comparison with the baselines.

In recent years, heterogeneous network/graph representation learning/embedding (HNE) has drawn tremendous attentions from research communities in multiple disciplines. HNE has shown its outstanding performances in various networked data analysis and mining tasks. In fact, most of real-world information networks in multiple fields can be modelled as the heterogeneous information networks (HIN). Thus, the HNE-based techniques can sufficiently capture rich-structured and semantic latent features from the given information network in order to facilitate for different task-driven learning tasks. This is considered as fundamental success of HNE-based approach in comparing with previous traditional homogeneous network/graph based embedding techniques. However, there are recent studies have also demonstrated that the heterogeneous network/graph modelling and embedding through graph neural network (GNN) is not usually reliable. This challenge is original come from the fact that most of real-world heterogeneous networks are considered as incomplete and normally contain a large number of feature noises. Therefore, multiple attempts have proposed recently to overcome this limitation. Within this approach, the meta-path-based heterogeneous graph-structured latent features and GNN-based parameters are jointly learnt and optimized during the embedding process. However, this integrated GNN and heterogeneous graph structure (HGS) learning approach still suffered a challenge of effectively parameterizing and fusing different graph-structured latent features from both GNN- and HGS-based sides into better task-driven friendly and noise-reduced embedding spaces. Therefore, in this paper we proposed a novel attention-supplemented heterogeneous graph structure embedding approach, called as: AGSE. Our proposed AGSE model supports to not only achieve the combined rich heterogeneous structural and GNN-based aggregated node representations but also transform achieved node embeddings into noise-reduced and task-driven friendly embedding space. Extensive experiments in benchmark heterogeneous networked datasets for node classification task showed the effectiveness of our proposed AGSE model in comparing with state-of-the-art network embedding baselines.

Behavioral targeting plays an important role in social media advertising for capturing users’ preferences of ads. While existing studies of behavioral targeting mainly focus on the user behaviors that have explicit correlations with ads, such as ad clicking and web search, many implicit relationships between users and ads, which reside in a variety of heterogeneous sources in social media platforms, are not utilized to enhance the prediction of users’ preferences of ads.In this paper, we propose a two-pronged approach to behavioral targeting that effectively addresses the above difficulties. First, we model the implicit relationships between users and ads as a heterogeneous information network (HIN), and propose a method that first performs representation learning in the HIN and then uses the learned representations to train a prediction model for boosting the performance of behavioral targeting. Second, we develop a human-in-the-loop framework to address the incompleteness challenge in HIN construction that may result in inferior performance of model prediction. The framework judiciously selects the most beneficial tasks to ask human for completing the HIN and utilizes the results from human to update the representation learning of HIN. We validate the effectiveness of our approach through extensive experiments on real datasets collected from WeChat, the largest social media platform in China. The experimental results show that our approach is effective at constructing a high-quality HIN at a low cost of human involvement, and the HIN can significantly improve the performance of social behavioral targeting.

Heterogeneous information networks contain heterogeneous types of nodes and edges, e.g., social networks and knowledge graphs. A meta-path is a path connecting nodes through a sequence of heterogeneous edges, representing different kinds of semantic relations among nodes. Meta-paths are good mechanisms to improve the quality of graph analysis on heterogeneous information networks. This paper presents an iceberg cube framework for heterogeneous information networks based on meta-paths. To the best of our knowledge, there is no such proposal in the past. (1) We use meta-paths to measure the similarities of nodes, and prove the problem is NP-hard. (2) An optimal solution is proposed for the strict case. We develop the variant of slice tree to aggregate networks hierarchically. (3) To improve the scalability, a general approximate algorithm is provided for fast aggregation, where random walk on meta-paths is employed to measure the similarities. (4) Two pruning strategies are designed for reducing search space when the aggregate function is monotonic. (5) Experiments on both real-world and synthetic networks demonstrate the effectiveness and efficiency of the algorithms.

Clustering and ranking have been successfully applied independently to homogeneous information networks, containing only one type of objects. However, real-world information networks are oftentimes heterogeneous, containing multiple types of objects and links. Recent research has shown that clustering and ranking can actually mutually enhance each other, and several techniques have been developed to integrate clustering and ranking together on a heterogeneous information network. To the best of our knowledge, however, all of such techniques assume the network follows a certain schema. In this paper, we propose a probabilistic generative model that simultaneously achieves clustering and ranking on a heterogeneous network that can follow arbitrary schema, where the edges from different types are sampled from a Poisson distribution with the parameters determined by the ranking scores of the nodes in each cluster. A variational Bayesian inference method is proposed to learn these parameters, which can be used to output ranking and clusters simultaneously. Our method is evaluated on both synthetic and real-world networks extracted from the DBLP and YELP data. Experimental results show that our method outperforms the state-of-the-art baselines.

A heterogeneous information network, which is composed of various types of nodes and edges, has a complex structure and rich information content, and is widely used in social networks, academic networks, e-commerce, and other fields. Link prediction, as a key task to reveal the unobserved relationships in the network, is of great significance in heterogeneous information networks. This paper reviews the application of presentation-based learning methods in link prediction of heterogeneous information networks. This paper introduces the basic concepts of heterogeneous information networks, and the theoretical basis of representation learning, and discusses the specific application of the deep learning model in node embedding learning and link prediction in detail. The effectiveness and superiority of these methods on multiple real data sets are demonstrated by experimental verification.

Recently, there is a surge of interests on heterogeneous information network analysis. Although evaluating the importance of objects has been well studied in homogeneous networks, it is not yet exploited in heterogeneous networks. In this paper, we study the ranking problem in heterogeneous networks and propose the HRank method to evaluate the importance of multiple types of objects and meta paths. A constrained meta path is proposed to subtly capture the rich semantics in heterogeneous networks. Since the importance of objects depends upon the meta paths in heterogeneous networks, HRank develops a path based random walk process. Furthermore, HRank can simultaneously determine the importance of objects and meta paths through applying the tensor analysis. Experiments on three real datasets show that HRank can effectively evaluate the importance of objects and paths together. Moreover, the constrained meta path shows its potential on mining subtle semantics by obtaining more accurate ranking results.

Content representation is a fundamental task in information retrieval. Representation learning is aimed at capturing features of an information object in a low-dimensional space. Most research on representation learning for heterogeneous information networks (HINs) focuses on static HINs. In practice, however, networks are dynamic and subject to constant change. In this article, we propose a novel and scalable representation learning model, M-DHIN , to explore the evolution of a dynamic HIN. We regard a dynamic HIN as a series of snapshots with different time stamps. We first use a static embedding method to learn the initial embeddings of a dynamic HIN at the first time stamp. We describe the features of the initial HIN via metagraphs, which retains more structural and semantic information than traditional path-oriented static models. We also adopt a complex embedding scheme to better distinguish between symmetric and asymmetric metagraphs. Unlike traditional models that process an entire network at each time stamp, we build a so-called change dataset that only includes nodes involved in a triadic closure or opening process, as well as newly added or deleted nodes. Then, we utilize the above metagraph-based mechanism to train on the change dataset. As a result of this setup, M-DHIN is scalable to large dynamic HINs since it only needs to model the entire HIN once while only the changed parts need to be processed over time. Existing dynamic embedding models only express the existing snapshots and cannot predict the future network structure. To equip M-DHIN with this ability, we introduce an LSTM-based deep autoencoder model that processes the evolution of the graph via an LSTM encoder and outputs the predicted graph. Finally, we evaluate the proposed model, M-DHIN , on real-life datasets and demonstrate that it significantly and consistently outperforms state-of-the-art models.