Multi-attributed Graph Research Articles

Multi-attribute Graph Convolution Network for Regional Traffic Flow Prediction

Multi-attribute Graph Convolution Network for Regional Traffic Flow Prediction

A fast algorithm for mining temporal association rules in a multi-attributed graph sequence

In real life, there exist a lot of attributed graphs each of which contains attribute information as well as structural information. As time goes on, a group of attributed graphs form an attributed graph sequence. Being the generalization of single-attributed graph sequences, multi-attributed graph sequences are arising vastly and quickly. Mining the temporal associations hidden in a multi-attributed graph sequence is in urgent need from data owners. To meet the need and fill the gap of research on mining such kind of temporal associations, we first give a definition of temporal association rules for describing temporal associations in a multi-attributed graph sequence, and then propose a fast algorithm for mining temporal association rules in a multi-attributed graph sequence which is based on the anti-monotonicity of support. The proposed algorithm is designed in two steps, namely finding frequent temporal association rules and verifying the credibility of these rules. Equipped with two novel joining and pruning strategies, the proposed algorithm exhibits much higher efficiency which is specially pursued in the process of rule mining. Experiments performed on synthetic datasets and real datasets show that the proposed algorithm is effective and more efficient than other existing algorithms.

Deep graph transformation for attributed, directed, and signed networks

Generalized from image and language translation, the goal of graph translation or transformation is to generate a graph of the target domain on the condition of an input graph of the source domain. Existing works are limited to either merely generating the node attributes of graphs with fixed topology or only generating the graph topology without allowing the node attributes to change. They are prevented from simultaneously generating both node and edge attributes due to: (1) difficulty in modeling the iterative, interactive, and asynchronous process of both node and edge translation and (2) difficulty in learning and preserving the inherent consistency between the nodes and edges in generated graphs. A general, end-to-end framework for jointly generating node and edge attributes is needed for real-world problems. In this paper, this generic problem of multi-attributed graph translation is named and a novel framework coherently accommodating both node and edge translations is proposed. The proposed generic edge translation path is also proven to be a generalization of existing topology translation models. Then, in order to discover and preserve the consistency of the generated nodes and edges, a spectral graph regularization based on our nonparametric graph Laplacian is designed. In addition, two extensions of the proposed model are developed for signed and directed graph translation. Lastly, comprehensive experiments on both synthetic and real-world practical datasets demonstrate the power and efficiency of the proposed method.

Sparse-Group Lasso for Graph Learning From Multi-Attribute Data

We consider the problem of inferring the conditional independence graph (CIG) of high-dimensional Gaussian vectors from multi-attribute data. Most existing methods for graph estimation are based on single-attribute models where one associates a scalar random variable with each node. In multi-attribute graphical models, each node represents a random vector. In this paper, we present a sparse-group lasso based penalized log-likelihood approach for graph learning from multi-attribute data. Existing works on multi-attribute graphical modeling have considered only group lasso penalty. The main objective of this paper is to explore the use of sparse-group lasso for multi-attribute graph estimation. An alternating direction method of multipliers (ADMM) algorithm is presented to optimize the objective function to estimate the inverse covariance matrix. Sufficient conditions for consistency and sparsistency of the estimator are provided. Numerical results based on synthetic as well as real data are presented.

Cognitive and Scalable Technique for Securing IoT Networks Against Malware Epidemics

The sheer volume of IoT networks being deployed today presents a major “attack surface” and poses significant security risks at a scale never encountered before. In other words, a single IoT device/node that gets infected with malware has the potential to spread the malicious activities across the network, eventually ceasing the network functionality or compromising the network. Simply detecting and quarantining the malware in IoT networks does not guarantee preventing malware propagation. On the other hand, use of traditional control theory for malware confinement is not effective, as most of the existing works do not consider real-time malware control strategies that can be implemented using uncertain infection information from the nodes in the network or have the containment problem decoupled from network performance. In response, in this work, we propose a two-pronged approach with malware detection at node-level, and confinement of malware at network-level. We deploy a recently proposed lightweight runtime malware detector at the node-level that employs Hardware Performance Counter (HPC) values for malware detection. This node-level malware information is combined with the malware propagation information and then fed during runtime to a stochastic predictive controller to confine the malware propagation without hampering the network performance. Synthesizing the node-level malware information with the model predictive containment strategy leads to achieving an average network throughput of nearly 200% of that of IoT network without any defense, and up to 160% of that of network with commonly employed state-of-the-art heuristic approaches for malware confinement. Furthermore, to scale with ever-increasing network topology sizes, we introduce a novel multi-attribute graph translation that can predict the network topology and node state information when provided with a snapshot of topology and node-level malware infection. The proposed multi-attribute graph translation has <5.88 Root Mean Square Error (RMSE) compared to the model predictive containment strategy and has shown nearly constant graph translation time and limited resource utilization independent of the network size.

Graph-Based Policy Change Detection and Implementation in SDN

Software Defined Networking (SDN), as a cutting-edge network, splits control and management planes from the data plane for simplifying network manageability as well as programmability. In SDN, network policies change with the passage of time due to changes in the application environment, topology or user/admin requirements. As a result, modifications at the control plane take place. In existing research works, packet violations occur due to already installed flow rules at the data plane (switches) that are not modified in case of a change of the Access Control List (ACL) policies at the SDN controller. There has been no research carried out that identifies packet violations and network inefficiencies in case of policy change. Our novel approach stores generated flow rules at the SDN controller and detects policy change, along with conflicting flow rules, to resolve the identified problem of policy change. Afterwards, the conflicting flow rules are removed from switches and new flow rules are installed along the new path according to new ACL policies. It helps to minimize packet violations, which increases network efficiency. In this research work, we deal with the inefficiencies of policy change detection with respect to access time, cost and space. In this regard, we used abstractions to formalize and detect network policies with the help of multi-attributed graphs. We utilized intent-based policies for the representation and implementation of our proposed approach. In addition, we used extended performance metrics for the analysis of our proposed approach. The simulation results show that our proposed approach performs better as compared to the existing approach, by varying the number of policy change and packet transmission rate. The results clearly indicate that our proposed approach helps to increase network performance and efficiency.

A survey on visualization approaches for exploring association relationships in graph data

Exploring relationships in complex datasets is one of the challenges in today’s big data era. The graph-based visualization approach, which integrates the advantages of graph analysis theory and visualization technologies and combines machine and human intelligence, has become an effective means for analyzing various relationships in complex datasets. In this paper, we first introduce a graph-based visual analytics model for associated data. Then, we summarize seven typical visualization methods for associated data according to their layout features, including their node-link diagram, adjacency matrix, hypergraph, flow diagram, graphs with geospatial information, multi-attribute graph, and space-filling diagram and discuss their advantages and disadvantages. We describe current graph simplification and interaction techniques, including graph filtering, node clustering, edge bundling, graph data dimension reduction, and topology-based graph transformation. Finally, we discuss the potential challenges and developmental trends of the research direction.

Detecting Communities and Correlated Attribute Clusters on Multi-Attributed Graphs

Detecting Communities and Correlated Attribute Clusters on Multi-Attributed Graphs

An Evolutionary Algorithm Coupled to an Outranking Method for the Multicriteria Shortest Paths Problem

In this article, we are interested in solving a combinatorial optimization problem, the shortest path problem in a multi-attribute graph, by the out-ranking methods. A multi-attribute graph has simultaneously qualitative and quantitative criteria. This situation gives rise to incomparable paths thus forming the Pareto front. Outranking methods in Multi-criteria Decision Making (MCDM) are the only methods that can take into account this situation (incomparability of actions). After presenting the categories of Multi-criteria Decision Making (MCDM) and the difficulties related to the problems of the shortest paths, we propose an evolutionary algorithm based on the outranking methods to solve the problem of finding “best” paths in a multi-attribute graph with non-additive criteria. Our approach is based on the exploration of induced subgraphs of the outranking graph. Properties have been established to serve as algorithmic basis. Numerical experiments have been carried out and the results presented in this article.

Exploiting multi-layer graph factorization for multi-attributed graph matching

Multi-layer graph matching methods effectively solve multi-attributed graph matching problems based on the multi-layer structure adopted to address the ambiguity and uncertainty arisen from the attribute integration. However, despite of its effectiveness for matching multi-attributed graphs, the approach has a long way to apply in the practical environment due to the scalability problem caused by a huge matrix for describing the multi-layer structure. In this paper, we propose a novel multi-layer graph matching algorithm based on the multi-layer graph factorization to address the issue. The main contribution of this research is three-fold. First, we propose a factorization method that decomposes the huge multi-layer matrix into several small matrices for efficiency. Second, we reformulate the original multi-layer matching problem into two relaxed problems by using the factorized matrices. Third, based on the relaxed problems, we propose a multi-layer graph matching algorithm inspired from the convex-concave relaxation procedure. In our extensive experiments on the synthetic and real-image datasets, the proposed method exhibits better performance than state-of-the-art algorithms based on the single-layer structure.

Consistent multiple graph matching with multi-layer random walks synchronization

We address the correspondence search problem among multiple graphs with complex properties while considering the matching consistency. We describe each pair of graphs by combining multiple attributes, then jointly match them in a unified framework. The main contribution of this paper is twofold. First, we formulate the global correspondence search problem of multi-attributed graphs by utilizing a set of multi-layer structures. The proposed formulation describes each pair of graphs as a multi-layer structure, and jointly considers whole matching pairs. Second, we propose a robust multiple graph matching method based on the multi-layer random walks framework. The proposed framework synchronizes movements of random walkers, and leads them to consistent matching candidates. In our extensive experiments, the proposed method exhibits robust and accurate performance over the state-of-the-art multiple graph matching algorithms.

Multi-Attributed Graph Matching With Multi-Layer Graph Structure and Multi-Layer Random Walks.

This paper addresses the multi-attributed graph matching problem, which considers multiple attributes jointly while preserving the characteristics of each attribute for graph matching. Since most of conventional graph matching algorithms integrate multiple attributes to construct a single unified attribute in an oversimplified manner, the information from multiple attributes is often not completely utilized. In order to solve this problem, we propose a novel multi-layer graph structure that can preserve the characteristics of each attribute in separated layers, and also propose a multi-attributed graph matching algorithm based on random walk centrality with the proposed multi-layer graph structure. We compare the proposed algorithm with other state-of-the-art graph matching algorithms based on a single-layer structure using synthetic and real data sets and demonstrate the superior performance of the proposed multi-layer graph structure and the multi-attributed graph matching algorithm.

Terrain classification based on spatial multi-attribute graph using Polarimetric SAR data

Recently the graph-based semi-supervised classification arouses vast amount of interest in remote sensing as it can utilize only a few labeled samples and large numbers of unlabeled samples to enhance the classification accuracy on various types of terrains. However in most of the conventional methods, multiple features (e.g. the scattering components, texture, color, etc) are concatenate together into a long vector for graph construction and classification. This not only ignores the physical attribute of features, but also causes so-called curse of dimensionality and limits the performances of classification. Inspired by the multi-view machine learning, we propose a spatial multi-attribute graph model and rank the attributes of polarimetric synthetic aperture radar (PolSAR) data for terrain classification in this paper. It firstly constructs multiple graphs according to the physical attributes of the groups of features based on different similarity metrics, then automatically optimizes a balanced weight for each graph and combines the spatial information between pixels for label propagation and classification. Since it takes into account of the physical properties from PolSAR data for feature fusion and graph construction, the mechanically feature stacking and curse of dimensionality can be avoided. Experimental results on synthesized PolSAR data and real ones show enhanced classification accuracy of the proposed method compared with state-of-the-art graph-based methods when only a small number of labeled samples are available. Our empirical studies also indicate that the covariance matrix play predominant roles for PolSAR classification.

A depression network of functionally connected regions discovered via multi-attribute canonical correlation graphs

To establish brain network properties associated with major depressive disorder (MDD) using resting-state functional magnetic resonance imaging (Rs-fMRI) data, we develop a multi-attribute graph model to construct a region-level functional connectivity network that uses all voxel level information. For each region pair, we define the strength of the connectivity as the kernel canonical correlation coefficient between voxels in the two regions; and we develop a permutation test to assess the statistical significance. We also construct a network based classifier for making predictions on the risk of MDD. We apply our method to Rs-fMRI data from 20 MDD patients and 20 healthy control subjects in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study. Using this method, MDD patients can be distinguished from healthy control subjects based on significant differences in the strength of regional connectivity. We also demonstrate the performance of the proposed method using simulationstudies.

Set-based unified approach for summarization of a multi-attributed graph

Rich availability of real world knowledge in a graph based on attributes of each vertex and its interactions, is a valuable source of information. However, it is hard to derive this useful knowledg...

Mining and Matching Relationships From Interaction Contexts in a Social Manufacturing Paradigm

There is an increasing use of social interaction contexts in the cross-enterprise manufacturing problem solving. To transform these massive and unstructured data into decision-support information for cross-enterprise manufacturing demand-capability matching, we present automated solutions to two phases: (1) extracting relationships based on a semi-supervised learning approach to derive formalized heterogeneous manufacturing network from the unstructured text-based context that contains high levels of noise and irrelevant information and (2) matching group-level relationships among the entities in the established manufacturing network. The extracting phase formulates network data using multiattributed graph that can encode various entities and relationships. The matching phase is based on probabilistic multiattributed graph matching, and implemented using distributed message passing algorithm. We developed a prototype system to verify the proposed model, which is also flexible to new domains of contexts and scale to large datasets. The ultimate goal of this paper is to facilitate knowledge transferring and sharing in the context of cross-enterprise social interaction, thereby supporting the integration of the resources and capabilities among different enterprise.

Analysis of the Usage of Magnetic Force-directed Approach and Visual Techniques for Interactive Context-based Drawing of Multi-attributed Graphs

Abstract In this article, the authors perform an analysis in order to assess adaptation of magnetic force-directed algorithms for context-based information extraction from multi-attributed graphs during visualization sessions. Theoretic standings behind magnetic force-directed approach are stated together with review on how particular features of respective algorithms in combination with appropriate visual techniques are especially suitable for improved processing and presenting of knowledge that is captured in form of graphs. The complexity of retrieving multi-attributed information within the proposed approach is handled with dedicated tools, such as selective attraction of nodes to MFE (Magnetic Force Emitter) based on search criteria, localization of POI (Point of Interest) regions, graph node anchoring, etc. Implicit compatibility of aforementioned tools with interactive nature of data exploration is distinguished. Description of case study, based on bibliometric network analysis is given, which is followed by the review of existing related works in this field. Conclusions are made and further studies in the field of visualization of multi-attributed graphs are defined.

Context-Sensitive Document Ranking

Ranking is a main research issue in IR-styled keyword search over a set of documents. In this paper, we study a new keyword search problem, called context-sensitive document ranking, which is to rank documents with an additional context that provides additional information about the application domain where the documents are to be searched and ranked. The work is motivated by the fact that additional information associated with the documents can possibly assist users to find more relevant documents when they are unable to find the needed documents from the documents alone. In tRis paper, a context is a multi-attribute graph, which can represent any information maintained in a relational database, where multi-attribute nodes represent tuples, and edges represent primary key and foreign key references among nodes. The context-sensitive ranking is related to several research issues, how to score documents, how to evaluate the additional information obtained in the context that may contribute to the document ranking, how to rank the documents by combining the scores/costs from the documents and the context. More importantly, the relationships between documents and the information stored in a relational database may be uncertain, because they are from different data sources, and the relationships are determined systematically using similarity match which causes uncertainty. In this paper, we concentrate ourselves on these research issues, and provide our solution on how to rank the documents in a context where there exist uncertainty between the documents and the context. We confirm the effectiveness of our approaches by conducting extensive experimental studies using real datasets. We present our findings in this paper.

Automated extraction of dimensional inspection features from part computer-aided design models

Metrological inspection planning is among the least explored computer-aided process planning (CAPP) domains. This paper examines the basic issues involved in automated dimensional inspection planning that works within an environment of a generic CAPP support system. A new algorithmic approach based on multi-attributed spatial graphs is developed for extracting inspection features. The features of specific interest to the planner are selected by applying a sequential filtering method. The method is capable of extracting many common types of inspection features (polyhedral, cylindrical and free-form) found in industrial practice.