Homogeneous Information Networks Research Articles

The impact of patentees assessment based on the heterogeneous patent innovation network

As a vital factor in technological innovation, patentee plays a significant role in the process of scientific and technological innovation, researching patentee has attracted the attention of experts and scholars. Previously, scholars have mainly quantified patent indicators or constructed homogeneous information networks to analyze patentees, but these methods cannot objectively measure the impact of patentees. Therefore, this study proposes a novel approach to assessing patentee impact based on a heterogeneous information network. The proposed method distinguishes the weight of different types of nodes using a weighted mechanism and extracts three types of fine-grained characteristics of network nodes. This approach results in the construction of a heterogeneous patent innovation network and the development of a new patentee impact assessment algorithm called CWAPN. Using Chinese green patents in the field of energy conservation and environmental protection as an example, experimental results show that the CWAPN algorithm can effectively assess the impact of patentees. Thereby identifying patentees who have made outstanding contributions to sustainable development in China.

Early Identification of Significant Patents Using Heterogeneous Applicant-Citation Networks Based on the Chinese Green Patent Data

With the deterioration of the environment and the acceleration of resource consumption, green patent innovation focusing on environmental protection fields has become a research hot-spot around the world. Previous researchers constructed homogeneous information networks to analyze the influence of patents based on citation ranking algorithms. However, a patent information network is a complex network containing multiple pieces of information (e.g., citation, applicant, inventor), and the use of a single information network will result in incomplete information or information loss, and the obtained results are biased. In addition, scholars constructed centrality indicators to assess the importance of patents with less consideration of the age bias problem of algorithms and models, and the results obtained are inaccurate. In this paper, based on the Chinese green patent (CNGP) dataset from 1985 to 2020, a CNGP heterogeneous applicant-citation network is constructed, and the rescaling method and normalization procedure are used to solve the age bias. The results illustrate that the method proposed in this paper is able to identify significant patents earlier, and the performance of the rescaled indegree (R_ID) works best such as the IR score is 17.32% in the top 5% of the rankings, and it is the best in the constructed dynamic heterogeneous networks as well. In addition, the constructed heterogeneous information network has better results compared with the traditional homogeneous information network, such as the NIR score of R_ID metrics can be improved by 2% under the same condition. Therefore, the analysis method proposed in this paper can reasonably evaluate the quality of patents and identify significant patents earlier, thus providing a new method for scientists to measure the quality of patents.

MFHE: Multi-View Fusion-Based Heterogeneous Information Network Embedding

Depending on the type of information network, information network embedding is classified into homogeneous information network embedding and heterogeneous information network (HIN) embedding. Compared with the homogeneous network, HIN composition is more complex and contains richer semantics. At present, the research on homogeneous information network embedding is relatively mature. However, if the homogeneous information network model is directly applied to HIN, it will cause incomplete information extraction. It is necessary to build a specialized embedding model for HIN. Learning information network embedding based on the meta-path is an effective approach to extracting semantic information. Nevertheless, extracting HIN embedding only from a single view will cause information loss. To solve these problems, we propose a multi-view fusion-based HIN embedding model, called MFHE. MFHE includes four parts: node feature space transformation, subview information extraction, multi-view information fusion, and training. MFHE divides HIN into different subviews based on meta-paths, models the local information accurately in the subviews based on the multi-head attention mechanism, and then fuses subview information through a spatial matrix. In this paper, we consider the relationship between subviews; thus, the MFHE is applicable to complex HIN embedding. Experiments are conducted on ACM and DBLP datasets. Compared with baselines, the experimental results demonstrate that the effectiveness of MFHE and HIN embedding has been improved.

Effects of the news finds me perception on algorithmic news attitudes and social media political homophily

AbstractPrior literature on political filter bubbles suggests an overall positive association between social media use and political networks diversification. Sometimes, this might not be the case. This study argues that the News Finds Me perception (NFM) or the belief that “one” can be well-informed about public affairs without actively seeking information as news will find “me” through “my” networks, tend to nurture a positive attitude toward algorithmic news gatekeeping. Likewise, NFM’s news over-reliance on one’s social network support the development of homogeneous information and discussion political networks in social media (political homophily). Results based on a variety of ordinary least squares regression models (cross-sectional, lagged, and autoregressive) from a U.S. representative panel survey, as we all as autoregressive structural equation model tests, indicate that this is indeed the case. This study serves to specifically clarify when and how social media and the NFM facilitate politically homogeneous filter bubbles.

A survey on heterogeneous network representation learning

Heterogeneous information networks usually contain different kinds of nodes and distinguishing types of relations, which can preserve more information than homogeneous information networks. Heterogeneous network representation learning attempts to learn a low-dimensional representation for each node and capture rich semantic information of the given network. Most of existing surveys focus on heterogeneous information network analysis and homogeneous information network representation learning. Although considerable research efforts concentrate on heterogeneous network representation learning, there are few surveys that systematically review the state-of-the-art heterogeneous network representation learning techniques. Motivated by this, we propose a taxonomy of heterogeneous network representation learning algorithms according to different approaches of capturing semantic information in heterogeneous networks, including path based algorithms and semantic unit based algorithms. Moreover, we introduce the typical heterogeneous network representation learning techniques in detail and make a comparative analysis of these techniques. In addition, the research challenges in terms of semantics preserving, data sparsity and scalability are discussed. To tackle these challenges, several potential future research directions for heterogeneous network representation learning are pointed out, including semantic relations extraction, dynamic heterogeneous networks, very large heterogeneous networks and heterogeneous networks construction.

Rich heterogeneous information preserving network representation learning

Network representation learning has attracted increasing attention recently due to its applicability in network analysis. However, most existing network representation learning models only focus on preserving fragmentary aspects of network information, either node proximities or fixed semantic information. In this paper, we propose a novel algorithm named Rich Heterogeneous Information Preserving Network Representation Learning (HIRL), which integrates the high-order proximity among nodes and semantic information into a generic framework by exploiting a flexible autoencoder network. Based on the proposed algorithm, we can explore the hidden information in heterogeneous information networks through any custom form of path schema, and represents different types of nodes in a continuous and common vector space. Moreover, the proposed HIRL is applicable to homogeneous information networks. Extensive experimental results demonstrate that our approach can effectively preserve the information in networks under various path schemas, and performs better on real-world applications such as network reconstruction, link prediction, and node classification compared with the state-of-the-art methods.

Hierarchy construction and classification of heterogeneous information networks based on RSDAEf

Heterogeneous information networks (HINs) composed of multiple types of nodes and links, play increasingly important roles in real life applications. Classification of the related data is an essential work in network analysis. Existing methods can effectively solve these classification tasks when they are applied to homogeneous information networks and simple data, but not for the noisy and sparse data. To address the problem, we propose Stacked Denoising Auto Encoder (SDAE) with sparse factors to learn features of nodes in heterogeneous networks. In particular, sparse factors are added in each hidden layer of the proposed stacked denoising auto-encoder to efficiently extract features from noisy and sparse data. Moreover, a relax strategy is employed to construct class hierarchy with high-quality based. Finally, nodes of the heterogeneous information network can be classified. Our proposed framework Relax strategy on Stacked Denoising Auto Encoder with sparse factors (RSDAEf) comparison with several existing methods clearly indicates RSDAEf outperforms the existing methods and achieves a classification precision of 88.3% on DBLP dataset.

$k$ -Context Technique: A Method for Identifying Dense Subgraphs in a Heterogeneous Information Network

Most real-life problems can be modeled using heterogeneous networks, as they consist of several interconnected entities. However, researchers often study most of these problems as projected homogeneous networks. In such networks, different entities can be merged, some entities can be removed, or interactions among some entities can be overlooked, which results in a significant information loss. Moreover, these networks often overlook different structural possibilities in heterogeneous networks. Different structural possibilities may include a hierarchical network topology or a centralized network topology . Mining on different structural topologies of a heterogeneous information network may lead to a better understanding of the behavior and characteristics of the individual entities and the network as a whole. This article proposes a novel approach named $k$ -context technique for extracting dense subgraphs from different structural topologies of the heterogeneous information network. The approach has been evaluated using both synthetic and real-life data sets, and it compared the results with an existing algorithm for finding dense subgraphs from a homogeneous information network.

DW-PathSim: a distributed computing model for topic-driven weighted meta-path-based similarity measure in a large-scale content-based heterogeneous information network

ABSTRACTFrom the past, several studies in the information network mining have been mainly designed for single-typed objects and links, called the homogeneous information network (HoIN). These HoIN-based approaches are definitely unsuitable for multi-typed objects and links, known as the heterogeneous information network (HIN). There is no doubt that most of the real-world networks are not only composed in a complex heterogeneous manner but also are extremely large in size. The big size of these networks is one of the most challenging issues that influence directly the system's performance. In this paper, our studies are mainly focused on improving the topic-driven weighted similarity measurement between same-typed objects in HIN, based on the meta-path-based mechanism, called W-PathSim. Moreover, our contributions in this paper also aim to optimize the performance of the W-PathSim model in the manner of very large-scaled HIN by combining the proposed W-PathSim model with the approach of distributed computing of ‘graph-frames’ on Spark, called DW-PathSim. The DW-PathSim not only supports in tackling the problem of weighted meta-path-based similarity searching in HINs but also the distributed computing problem on the big networked data. We test the DW-PathSim model with the real-world DBLP dataset in order to demonstrate the effectiveness of our proposed models.

Generative Adversarial Network Based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation

Network representation has been recently exploited for many applications, such as citation recommendation, multi-label classification and link prediction. It learns low-dimensional vector representation for each vertex in networks. Existing network representation methods only focus on incomplete aspects of vertex information (i.e., vertex content, network structure or partial integration), moreover they are commonly designed for homogeneous information networks where all the vertices of a network are of the same type. In this paper, we propose a deep network representation model that integrates network structure and the vertex content information into a unified framework by exploiting generative adversarial network, and represents different types of vertices in the heterogeneous network in a continuous and common vector space. Based on the proposed model, we can obtain heterogeneous bibliographic network representation for efficient citation recommendation. The proposed model also makes personalized citation recommendation possible, which is a new issue that a few papers addressed in the past. When evaluated on the AAN and DBLP datasets, the performance of the proposed heterogeneous bibliographic network based citation recommendation approach is comparable with that of the other network representation based citation recommendation approaches. The results also demonstrate that the personalized citation recommendation approach is more effective than the non-personalized citation recommendation approach.

Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition

The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.

A Tensor CP Decomposition Method for Clustering Heterogeneous Information Networks via Stochastic Gradient Descent Algorithms

Clustering analysis is a basic and essential method for mining heterogeneous information networks, which consist of multiple types of objects and rich semantic relations among different object types. Heterogeneous information networks are ubiquitous in the real-world applications, such as bibliographic networks and social media networks. Unfortunately, most existing approaches, such as spectral clustering, are designed to analyze homogeneous information networks, which are composed of only one type of objects and links. Some recent studies focused on heterogeneous information networks and yielded some research fruits, such as RankClus and NetClus. However, they often assumed that the heterogeneous information networks usually follow some simple schemas, such as bityped network schema or star network schema. To overcome the above limitations, we model the heterogeneous information network as a tensor without the restriction of network schema. Then, a tensor CP decomposition method is adapted to formulate the clustering problem in heterogeneous information networks. Further, we develop two stochastic gradient descent algorithms, namely, SGDClus and SOSClus, which lead to effective clustering multityped objects simultaneously. The experimental results on both synthetic datasets and real-world dataset have demonstrated that our proposed clustering framework can model heterogeneous information networks efficiently and outperform state-of-the-art clustering methods.

HeteClass: A Meta-path based framework for transductive classification of objects in heterogeneous information networks

Transductive classification using labeled and unlabeled objects in a heterogeneous information network for knowledge extraction is an interesting and challenging problem. Most of the real-world networks are heterogeneous in their natural setting and traditional methods of classification for homogeneous networks are not suitable for heterogeneous networks. In a heterogeneous network, various meta-paths connecting objects of the target type, on which classification is to be performed, make the classification task more challenging. The semantic of each meta-path would lead to the different accuracy of classification. Therefore, weight learning of meta-paths is required to leverage their semantics simultaneously by a weighted combination. In this work, we propose a novel meta-path based framework, HeteClass, for transductive classification of target type objects. HeteClass explores the network schema of the given network and can also incorporate the knowledge of the domain expert to generate a set of meta-paths. The regularization based weight learning method proposed in HeteClass is effective to compute the weights of symmetric as well as asymmetric meta-paths in the network, and the weights generated are consistent with the real-world understanding. Using the learned weights, a homogeneous information network is formed on target type objects by the weighted combination, and transductive classification is performed. The proposed framework HeteClass is flexible to utilize any suitable classification algorithm for transductive classification and can be applied on heterogeneous information networks with arbitrary network schema. Experimental results show the effectiveness of the HeteClass for classification of unlabeled objects in heterogeneous information networks using real-world data sets.

Analysis of a Homogeneous Information Network Efficiency with Due Regard for Risk Factors

Analysis of a Homogeneous Information Network Efficiency with Due Regard for Risk Factors