Heterogeneous Graph Neural Networks for Software Effort Estimation

RA-HGNN: Attribute completion of heterogeneous graph neural networks based on residual attention mechanism

A relation-aware heterogeneous graph convolutional network for relationship prediction

Malicious activities on the Internet are one of the most dangerous threats to users and organizations. Because of the flexibility a nd accessibility of domains, cyber criminals often utilize them to launch cyber attacks such as phishing or malware. Most of traditional malicious domain detection methods rely on feature engineering to learn the patterns of malicious domains. However, these methods can be easily evaded by some sophisticated evasion techniques such as Domain-flux or Fast-flux. Some recent studies utilized graph-based models to infer malicious domains and achieved better performance, yet without a fine-grained modeling of DNS scenarios. In this paper, we propose an attributed heterogeneous graph neural network model, GAMD, to detect malicious domains in a semi-supervised learning paradigm. Concretely, we utilize attributed heterogeneous information network to model the DNS scenarios with different types of nodes including domain, host, resolved-IP and different types of relation, such as request and resolution relations. We then design a fine-grained node type-aware feature transformation and edge type-aware aggregation mechanism to fuse the node attributes and structure information simultaneously and complete the inference over DNS graphs. In the experiments, we evaluate the performance of our model on a large-scale realworld passive DNS data and show that the proposed method outperforms the state-of-the-art in most evaluation metrics.

Dual-neighbourhood information aggregation and feature fusion for prediction of miRNA–disease association

The simultaneous use of two or more drugs due to multi-disease comorbidity continues to increase, which may cause adverse reactions between drugs that seriously threaten public health. Therefore, the prediction of drug-drug interaction (DDI) has become a hot topic not only in clinics but also in bioinformatics. In this study, we propose a novel pre-trained heterogeneous graph neural network (HGNN) model named HetDDI, which aggregates the structural information in drug molecule graphs and rich semantic information in biomedical knowledge graph to predict DDIs. In HetDDI, we first initialize the parameters of the model with different pre-training methods. Then we apply the pre-trained HGNN to learn the feature representation of drugs from multi-source heterogeneous information, which can more effectively utilize drugs' internal structure and abundant external biomedical knowledge, thus leading to better DDI prediction. We evaluate our model on three DDI prediction tasks (binary-class, multi-class and multi-label) with three datasets and further assess its performance on three scenarios (S1, S2 and S3). The results show that the accuracy of HetDDI can achieve 98.82% in the binary-class task, 98.13% in the multi-class task and 96.66% in the multi-label one on S1, which outperforms the state-of-the-art methods by at least 2%. On S2 and S3, our method also achieves exciting performance. Furthermore, the case studies confirm that our model performs well in predicting unknown DDIs. Source codes are available at https://github.com/LinsLab/HetDDI.

Social network analysis has typically concerned analysis of one type of tie connecting nodes of the same type. It has however been recognised that people are connected through multiple types of ties and that people in addition are affiliated with multiple types of non-people nodes. Exponential random graph models (ERGM) is a family of statistical models for social networks that at this point allows for a number of different types of network data, including one-mode networks, bipartite networks, multiplex data, as well as multilevel network data. Multilevel networks have been proposed as a joint representation of associations between multiple types of entities or nodes, such as people and organization, where two types of nodes gives rise to three distinct types of ties. The typical roster data collection method may be impractical or infeasible when the node sets are hard to detect or define or because of the cognitive demands on respondents. Multilevel multilayered networks allow us to consider a multitude of different sources of data and to sample on different types of nodes and relations. We consider modelling multilevel multilayered networks using exponential random graph models and extend a recently developed Bayesian data-augmentation scheme to allow for partially missing data. We illustrate the proposed inference procedures for the case of multilevel snowball sampling and sampling with error based on the Noordin Top network.

Global mobile data traffic reached 11.5 exabyte per month at the end of 2017, up from 6.7 exabyte per month at the end of 2016. Therefore to keep-up with this ever-increasing data demand, Heterogeneous network seams to be the most appropriate resolution. A Heterogeneous network consists of a combination of different types of nodes (small-cells and macrocells) that share coverage regions and may use different radio access technologies. Whenever a user in active connection moves from one cell to the other, the network must transfer the connection to a new cell to maintain the communication service in a process called handover. However this handover process must be seamless. Some key handover challenges and design issues for the next generation of wireless networks was discussed in this review.

Network and linked data have become quite prevalent in recent years because of the ubiquity of the web and social media applications, which are inherently network oriented. Such networks are massive, dynamic, contain a lot of content, and may evolve over time. In this paper, we will study the problem of efficient dynamic link inference in temporal and heterogeneous information networks. The problem of efficiently performing dynamic link inference is extremely challenging in massive and heterogeneous information network because of the challenges associated with the dynamic nature of the network, and the different types of nodes and attributes in it. Both the topology and type information need to be used effectively for the link inference process. We propose an effective two‐level scheme which makes efficient macro‐ and micro‐decisions for combining structure and content in a dynamic and time‐sensitive way. The time‐sensitive nature of the links is leveraged in order to perform effective link prediction. We will also study how to apply the method to the problem of community prediction. We illustrate the effectiveness of our technique over a number of real data sets. Statistical Analysis and Data Mining 2013 DOI: 10.1002/sam.11198

Considering the characteristic of heterogeneous wireless sensor networks, the clustering algorithm based on data content for heterogeneous sensor network is proposed. In cluster construction stage, according to the various sensor node types in CADC algorithm, the same types of nodes is divided into the same cluster and different types of nodes can not be divided into the same cluster. In the cluster heads selection scheme, nodes with the most residual energy will be elected cluster heads first. Cluster heads with the same type further integrate into the secondary cluster. Because this algorithm makes the data aggregation to be the most optimal and reduces the transmission distance of cluster heads, it can prolong the lifetime of the whole networks.

Railway network delay evolution: A heterogeneous graph neural network approach

Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its metapath based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.

Evolving heterogeneous networks, which contain different types of nodes and links that change over time, appear in many domains including protein---protein interactions, scientific collaborations, telecommunications. In this paper, we aim to discover temporal information from a heterogenous evolving network in order to improve node classification. We propose a framework, Genetic Algorithm enhanced Time Varying Relational Classifier for evolving Heterogeneous Networks (GA-TVRC-Het), to extract the effects of different relationship types in different time periods in the past. These effects are discovered adaptively by utilizing genetic algorithms. A relational classifier is extended as the classification method in order to be able to work with different types of nodes. The proposed framework is tested on two real world data sets. It is shown that using the optimal time effect improves the classification performance to a large extent. It is observed that the optimal time effect does not necessarily follow a certain functional trend, for example linear or exponential decay in time. Another observation is that the optimal time effect may be different for each type of interaction. Both observations reveal the reason why GA-TVRC-Het outperforms methods that rely on a predefined form of time effect or the same time effect for each link type.

User interfaces (UI) of desktop, web, and mobile applications involve a hierarchy of objects (e.g., applications, screens, view class, and other types of design objects) with multimodal (e.g., textual and visual) and positional (e.g., spatial location, sequence order, and hierarchy level) attributes. We can therefore represent a set of application UIs as a heterogeneous network with multimodal and positional attributes. Such a network not only represents how users understand the visual layout of UIs but also influences how users would interact with applications through these UIs. To model the UI semantics well for different UI annotation, search, and evaluation tasks, this article proposes the novel Heterogeneous Attention-based Multimodal Positional (HAMP) graph neural network model. HAMP combines graph neural networks with the scaled dot-product attention used in transformers to learn the embeddings of heterogeneous nodes and associated multimodal and positional attributes in a unified manner. HAMP is evaluated with classification and regression tasks conducted on three distinct real-world datasets. Our experiments demonstrate that HAMP significantly out-performs other state-of-the-art models on such tasks. To further provide interpretations of the contribution of heterogeneous network information for understanding the relationships between the UI structure and prediction tasks, we propose Adaptive HAMP (AHAMP), which adaptively learns the importance of different edges linking different UI objects. Our experiments demonstrate AHAMP’s superior performance over HAMP on a number of tasks, and its ability to provide interpretations of the contribution of multimodal and positional attributes, as well as heterogeneous network information to different tasks.

The social network is a powerful data structure allowing the study of relationship information between entities. The existing methods only rely on analyzing homogeneous social network assuming only a single type of node and relation. Though, real world complex networks are not so. Usually it is a heterogeneous social network which assumes a network with different types of nodes and relation. A Multi Relational Network (MRN) is used to describe the nodes and links in the form of a directional semantic graph where each node is related with more than one relationship with others. The profile for each node can be generated based on the relation sequence of each node. To analyze the relation of node in the heterogeneous social network, role analysis is done in the profile creation. The evaluations are conducted on a real-world movie dataset with promising results.

Robustness of interdependent supply chain networks against both functional and structural cascading failures