Mining knowledge from databases

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.

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.

Measuring diversity in heterogeneous information networks

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

Multi-type objects with multi-type relations are ubiquitous in real-world networks, e.g. bibliographic networks. Such networks are also called heterogeneous information networks. However, the research on clustering for heterogeneous information networks is little. A new algorithm, called NetClus, has been proposed in recent two years. Although NetClus is applied on a heterogeneous information network with a star network schema, considering the relations between center objects and all attribute objects linking to them, it ignores the relations between center objects such as citation relations, which also contain rich information. Hence, we think the star network schema cannot be used to characterize all possible relations without integrating the linkage structure among center objects, which we call the Center Linkage Structure, and there has been no practical way good enough to solve it. In this paper, we present a novel algorithm, BibClus, for clustering heterogeneous objects with center linkage structure by taking a bibliographic information network as an example. In BibClus, we build a probabilistic model of pair wise hidden Markov random field (P-HMRF) to characterize the center linkage structure, and convert it to a factor graph. We further combine EM algorithm with factor graph theory, and design an efficient way based on message passing algorithm to inference marginal probabilities and estimate parameters at each iteration of EM. We also study how factor functions affect clustering performance with different function forms and constraints. For evaluating our proposed method, we have conducted thorough experiments on a real dataset that we had crawled from ACM Digital Library. The experimental results show that BibClus is effective and has a much higher quantity than the recently proposed algorithm, NetClus, in both recall and precision.

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.

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.

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.

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.

News data is one of the most abundant and familiar data sources. News data can be systematically utilized and ex- plored by database, data mining, NLP and information re- trieval researchers to demonstrate to the general public the power of advanced information technology. In our view, news data contains rich, inter-related and multi-typed data objects, forming one or a set of gigantic, interconnected, het- erogeneous information networks. Much knowledge can be derived and explored with such an information network if we systematically develop effective and scalable data-intensive information network analysis technologies. By further developing a set of information extraction, in- formation network construction, and information network mining methods, we extract types, topical hierarchies and other semantic structures from news data, construct a semi- structured news information network NewsNet. Further, we develop a set of news information network exploration and mining mechanisms that explore news in multi-dimensional space, which include (i) OLAP-based operations on the hierarchical dimensional and topical structures and rich-text, such as cell summary, single dimension analysis, and promo- tion analysis, (ii) a set of network-based operations, such as similarity search and ranking-based clustering, and (iii) a set of hybrid operations or network-OLAP operations, such as entity ranking at different granularity levels. These form the basis of our proposed NewsNetExplorer system. Although some of these functions have been studied in recent research, effective and scalable realization of such functions in large networks still poses multiple challenging research problems. Moreover, some functions are our on-going research tasks. By integrating these functions, NewsNetExplorer not only provides with us insightful recommendations in NewsNet exploration system but also helps us gain insight on how to perform effective information extraction, integration and mining in large unstructured datasets.

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.

Online bibliographic databases, such as DBLP in computer science and PubMed in medical sciences, contain abundant information about research publications in different fields. Each such database forms a gigantic information network (hence called BibNet), connecting in complex ways research papers, authors, conferences/journals, and possibly citation information as well, and provides a fertile land for information network analysis. Our BibNetMiner is designed for sophisticated information network mining on such bibliographic databases. In this demo, we will take the DBLP database as an example, demonstrate several attractive functions of BibNetMiner, including clustering, ranking and profiling of conferences and authors based on the research subfields. A user-friendly, visualization-enhanced interface will be provided to facilitate interactive exploration of a bibliographic database. This project will serve as an example to demonstrate the power of links in information network mining. Since the dataset is large and the network is heterogeneous, such a study will benefit the research on the analysis of massive heterogeneous information networks.

With the advent of the Internet era, the recommendation system has developed rapidly. Heterogeneous information networks representation learning is widely used in recommendation systems due to its advantages in complex information modeling. Although the performance of the recommendation system has been improved, there are still two shortcomings: 1. There is a lot of noise data in the instances of the meta-path generated by random walks of meta-paths, which will reduce the performance of the recommendation system. 2. Traditional recommendation algorithms fail to make full use of the relevant meta-path information in heterogeneous information networks, which makes the recommendation results lack of interpretability. To solve these problems, we propose a recommendation system based on heterogeneous network representation learning and neural network model. Firstly, use the matrix factorization and the similarity calculation to select the meta-path instances with good quality as the pre-training vectors of the recommendation system. Then, we combine LSTM with Attention mechanism to learn the user, item and meta-path embeddings, and use MLP to make prediction after fusion to jointly improve the recommendation effect. We conducted experiments on Movielens datasets to evaluate the performance of our proposed recommendation.

Online social networks, World Wide Web, media, and technological networks, and other types of so-called information networks are ubiquitous nowadays. These information networks are inherently heterogeneous and dynamic . They are heterogeneous as they consist of multi-typed objects and relations, and they are dynamic as they are constantly evolving over time. One of the challenging issues in such heterogeneous and dynamic environments is to forecast those relationships in the network that will appear in the future. In this article, we try to solve the problem of continuous-time relationship prediction in dynamic and heterogeneous information networks. This implies predicting the time it takes for a relationship to appear in the future, given its features that have been extracted by considering both heterogeneity and temporal dynamics of the underlying network. To this end, we first introduce a feature extraction framework that combines the power of meta-path-based modeling and recurrent neural networks to effectively extract features suitable for relationship prediction regarding heterogeneity and dynamicity of the networks. Next, we propose a supervised non-parametric approach, called Non-Parametric Generalized Linear Model (N p -G lm ), which infers the hidden underlying probability distribution of the relationship building time given its features. We then present a learning algorithm to train N p -G lm and an inference method to answer time-related queries. Extensive experiments conducted on synthetic data and three real-world datasets, namely Delicious, MovieLens, and DBLP, demonstrate the effectiveness of N p -G lm in solving continuous-time relationship prediction problem vis-à-vis competitive baselines.

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.