Probabilistic Graph Research Articles

Analysis of Sanda action based on CCNN model of IBS distance measurement

IBS is a similarity measurement method of symbol sequences based on order statistics, and CCNN is a method based on CNN feature extraction and IBS distance measurement, which can construct crossover probability graph through image crossover comparison. IBS distance and Noribs distance are used to define the similarity between crossover probability graphs. For Sanda movement, it is difficult to carry out big data analysis with a small number of samples and the movement style of athletes will change greatly. In this paper, a CCNN model based on IBS distance measurement is proposed to analyze the action of Sanda. Firstly, the motion pictures obtained were de-noised and then simplified into a sequence diagram of human skeleton nodes by Delaunay triangulation. Then, 80% of the samples were input into the CCNN network for training and 20% for algorithm verification. The experiment found that in the case of a small number of samples, the accuracy of the simplified image analysis was 10% higher than that of the unsimplified image. In addition, compared with the accuracy of K-distance classification, Bayesian network classification and decision tree classification, it is found that the model can improve the accuracy by 2%-11.75% due to the small number of samples, and has higher accuracy and applicability.

A cognitive diagnostic framework for computer science education based on probability graph model

A cognitive diagnostic framework for computer science education based on probability graph model

Bayesian damage recognition in document images based on a joint global and local homogeneity model

Physical damages (such as torn-offs and scratches) are commonly seen in historical documents. Recognition of such damages is currently absent in digitization-and-information-extraction (DIE) systems but crucial for automatic document comprehension and exploitation. In this paper we propose a generic damage recognition (DR) method based on a joint global and local modeling of the text homogeneity (TH) pattern exhibited in document images. More specifically, a connected component (CC) based formulation is developed as a global homogeneity measure, where TH is characterized using a probabilistic graph model for a coarse recognition of damaged regions. A multi-resolution analysis (MRA) of TH is further developed for a granular within-CC recognition of damage pixels, where the disparity between damage and text pixels is characterized by exploiting neighborhood transitions. This enables the formulation of a local homogeneity measure, where the neighborhood transition around an individual pixel is modeled using the propagation of the approximation coefficients of a stationary wavelet transform (SWT). The proposed global and local homogeneity measures are integrated as a joint likelihood in a Bayesian model with a Markov random field (MRF) prior, where DR is formulated as a maximum a posterior (MAP) inference which is addressed using Markov Chain Monte Carlo (MCMC) sampling. The resulting algorithm is tested on a set of real-life historical newspaper images containing damages of varying size and shape. The performance of the algorithm is evaluated using both F-measures and the Intersection-over-Union (IoU) metric, where test results demonstrate the promising potential of the proposed method.

Probabilistic Dependency Graphs

We introduce Probabilistic Dependency Graphs (PDGs), a new class of directed graphical models. PDGs can capture inconsistent beliefs in a natural way and are more modular than Bayesian Networks (BNs), in that they make it easier to incorporate new information and restructure the representation. We show by example how PDGs are an especially natural modeling tool. We provide three semantics for PDGs, each of which can be derived from a scoring function (on joint distributions over the variables in the network) that can be viewed as representing a distribution's incompatibility with the PDG. For the PDG corresponding to a BN, this function is uniquely minimized by the distribution the BN represents, showing that PDG semantics extend BN semantics. We show further that factor graphs and their exponential families can also be faithfully represented as PDGs, while there are significant barriers to modeling a PDG with a factor graph.

High-Order Markov Random Field as Attention Network for High-Resolution Remote-Sensing Image Compression

Content-weighted compression scheme for high-resolution remote-sensing (RS) images can be well modeled by Markov random field (MRF)-oriented attention. This article addresses high-resolution RS image compression by incorporating MRF into attention mechanism. To this end, we reformulate the attention mechanism with MRF-based probabilistic graph modeling implicitly and combine the target of image compression and parameter learning of MRF in a unified framework, namely high-order MRF-oriented attention (HMA) network. Specifically, HMA extends key-value query (KVQ) pairwise terms of the vanilla attention to high-order terms, by which the prior information could be expressed effectively to boost performance of high-resolution RS image compression. It is noted that several superiorities of HMA are listed. First, unlike the vanilla attention network that apt to yield coarse features, HMA is capable of output more pleasing decoding results. Second, HMA can accelerate the convergence in the training of the deep neural networks (DNNs), thus facilitating deploying it on resource-limited IOT devices. Third, HMA demonstrates its potential of processing semantic joint task. Moreover, We thoroughly evaluate our approach on standard data sets of varying resolutions, the proposed framework performs favorably against most image coding standards and DNN-based codecs on the ISPRS Vaihingen data set and the USC-SIPI data set especially at low bit rates.

Topological Aberrance of Structural Brain Network Provides Quantitative Substrates of Post-Traumatic Brain Injury Attention Deficits in Children.

Background: Traumatic brain injury (TBI)-induced attention deficits are among the most common long-term cognitive consequences in children. Most of the existing studies attempting to understand the neuropathological underpinnings of cognitive and behavioral impairments in TBI have utilized heterogeneous samples and resulted in inconsistent findings. The current research proposed to investigate topological properties of the structural brain network in children with TBI and their relationship with post-TBI attention problems in a more homogeneous subgroup of children who had severe post-TBI attention deficits (TBI-A). Materials and Methods: A total of 31 children with TBI-A and 35 group-matched controls were involved in the study. Diffusion tensor imaging-based probabilistic tractography and graph theoretical techniques were used to construct the structural brain network in each subject. Network topological properties were calculated in both global level and regional (nodal) level. Between-group comparisons among the topological network measures and analyses for searching brain-behavioral were all corrected for multiple comparisons using Bonferroni method. Results: Compared with controls, the TBI-A group showed significantly higher nodal local efficiency and nodal clustering coefficient in left inferior frontal gyrus and right transverse temporal gyrus, whereas significantly lower nodal clustering coefficient in left supramarginal gyrus and lower nodal local efficiency in left parahippocampal gyrus. The temporal lobe topological alterations were significantly associated with the post-TBI inattentive and hyperactive symptoms in the TBI-A group. Conclusion: The results suggest that TBI-related structural re-modularity in the white matter subnetworks associated with temporal lobe may play a critical role in the onset of severe post-TBI attention deficits in children. These findings provide valuable input for understanding the neurobiological substrates of post-TBI attention deficits, and have the potential to serve as quantitatively measurable criteria guiding the development of more timely and tailored strategies for diagnoses and treatments to the affected individuals. Impact statement This study provides a new insight into the neurobiological substrates associated with post-traumatic brain injury attention deficits (TBI-A) in children, by evaluating topological alterations of the structural brain network. The results demonstrated that relative to group-matched controls, the children with TBI-A had significantly altered nodal local efficiency and nodal clustering coefficient in temporal lobe, which strongly linked to elevated inattentive and hyperactive symptoms in the TBI-A group. These findings suggested that white matter structural re-modularity in subnetworks associated with temporal lobe may serve as quantitatively measurable biomarkers for early prediction and diagnosis of post-TBI attention deficits in children.

A graphical decomposition and similarity measurement approach for topic detection from online news

• We propose a Capsule Semantic Graph (CSG) to represent the news documents. • The CSG can effectively capture the relationship between words and semantic of news documents. • We introduce the graph kernel to measure the similarity between CSGs. • Our method can better solve the problems of news representation and similarity measurement. • Our method has great significance for topic detection from news. Topic detection aims to discover valuable topics from the massive online news. It can help people to capture what is happening in real world and alleviate the burden of information overload. It also has great significance since the online news is experiencing an explosive growth. Topic detection is typically transformed into a document clustering problem, whose core idea is to cluster news documents that report on the same topic to the same group based on document similarity. Due to the complex structure and long length of news documents, the similarity measurement of news is very challenging. Existing term-based methods represent news documents based on a set of informative keywords in the document with a vector space model (VSM) and then the relationship between documents is calculated by cosine similarity . However, VSM ignores the relationship between words and has sparse semantics, which leads to low precision of topic detection. In recent years, the probabilistic methods and the graph analytical methods have been proposed for topic detection. However, both of them have high time complexity. To cope with these problems, we first present a novel document representation approach based on graphical decomposition, which decomposes each news document into different semantic units and then relationship between the semantic units is constructed to form a capsule semantic graph (CSG). The CSG can retain the relationship between words and alleviate the sparse semantics compared to VSM representation. We next introduce the graph kernel to measure the similarity between the CSGs based on their substructures. Finally, we use an incremental clustering method to cluster the news documents, in which the documents are represented by CSGs and the similarity between documents is calculated by graph kernel. The experiment results on three standard datasets show that our method obtains higher precision, recall and F1 score than several state-of-the-art methods. Moreover, the experiment results on a large news dataset show that our CSG-SM has lower time complexity than probabilistic methods and graph analytical methods.

Probabilistic Modeling of Information Dynamics in Networked Cyber–Physical–Social Systems

Cyber–physical–social systems (CPSS) are physical devices with highly integrated functions of sensing, computing, communication, and control, and are seamlessly embedded in human society. The levels of intelligence and functions that CPSS can perform rely on their extensive collaboration and information sharing through networks. In this article, information diffusion within CPSS networks is studied. Information dynamic models are proposed to characterize the evolution of information processing and decision-making capabilities of individual CPSS nodes. The data-driven statistical models are based on a mesoscale probabilistic graph model, where the individual nodes’ sensing and computing functions are represented as the probabilities of correct predictions; whereas the communication functions are represented as the probabilities of mutual influences between nodes. A copula dynamics model is proposed to explicitly capture the information dependency among individuals with joint prediction probabilities and estimated from extremal probabilities. A topology-informed vector autoregression model is proposed to represent the mutual influence of prediction capabilities. A spatial–temporal hybrid Gaussian process regression model is also proposed to simultaneously capture correlations between nodes and temporal correlation in the time series.

WLFS: Weighted label fusion learning framework for glioma tumor segmentation in brain MRI

Glioma is a common type of tumor that develops in the brain. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in cancer detection is a challenging task in cancer detection. In recent researches, the combination of atlas-based segmentation and machine learning methods have presented superior performance over other automatic brain MRI segmentation algorithms. To overcome the side effects of limited existing information on atlas-based segmentation, and the long training and the time consuming phase of learning methods, we proposed a semi-supervised learning framework by introducing a probabilistic graph based method. It combines the advantages of label propagation and patch-based segmentation on a parametric graph.To evaluate the proposed framework, we apply it to publicly available BRATS datasets, including low and high-grade glioma tumors. The experimental results show that the proposed framework has accurate segmentation results. Compared with the state-of-the-art methods, the proposed framework could obtain the best dice score for segmenting the “whole tumor” (WT) and “tumor core” (TC) regions. The segmentation result of the “enhancing active tumor” (ET) region is similar to the most recent works compared in this paper.

Clustering probabilistic graphs using neighbourhood paths

Probabilistic graphs have gained much interest in the data mining community since the big data revolution. Graph clustering is a widely used technique in exploratory data analysis such as data compression, information retrieval, protein–protein interaction network, etc. Traditional clustering algorithms assume that the data represented in a graph is deterministic, i.e., the edges between nodes are certain, though they may vary in their importance (weight). In probabilistic graphs, however, edges are uncertain and are represented as probabilities. In this work, we propose an evolutionary algorithm based clustering technique specifically to deal with uncertainties in such data. It has been observed that correlation usually exists among adjacent edges. Our work exploits this principle and explores the paths between nodes (including those of multiple order) to provide an estimate of the similarity between nodes with probabilistic edges. We embed this information into the fitness function and use Genetic algorithm to optimize the solution. The algorithm is tested on several benchmark datasets and compared with recent state-of-the-art algorithms.

Distributed Core Decomposition in Probabilistic Graphs

This paper initializes distributed algorithm studies for core decomposition in probabilistic graphs. Core decomposition has been proven to be a useful primitive for a wide range of graph analyses, but it has rarely been studied in probabilistic graphs, especially in a distributed environment. In this work, under a distributed model underlying Pregel and live distributed systems, we present the first known distributed solutions for core decomposition in probabilistic graphs, where there is an existence probability for each edge. In the scenario that the existence probability of edges are known to nodes, the proposed algorithm can get the exact coreness of nodes with a high probability guarantee. The proposed algorithm can also be used to efficiently update the coreness of nodes in dynamic graphs, where a set of edges are inserted/deleted into/from the graph. In the harsher case that the existence probability is unknown, we present a novel method to estimate the existence probability of edges, based on which the coreness of nodes with small approximation ratio guarantee can be computed. Extensive experiments are conducted on different types of real-world graphs and synthetic graphs. The results illustrate that the proposed algorithms exhibit good efficiency, stability and scalability.

Variational Instance-Adaptive Graph for EEG Emotion Recognition

The individual differences and the dynamic uncertain relationships among different electroencephalogram (EEG) regions are essential factors that limit EEG emotion recognition. To address these issues, in this paper, we propose a variational instance-adaptive graph method (V-IAG) that simultaneously captures the individual dependencies among different EEG electrodes and estimates the underlying uncertain information. Specifically, we employ two branches, i.e., instance-adaptive branch and variational branch, to construct the graph. Inspired by the attention mechanism, the instance-adaptive branch generates the graph based on the input so as to characterize the individual dependencies among EEG channels. The variational branch generates the probabilistic graph, which quantifies the uncertainties. We combine these two types of graphs to extract more discriminative features. To present more precise graph representation, we propose a new operation named the multi-level and multi-graph convolution operation, which aggregates the features of EEG channels from different frequencies with different graphs. Furthermore, we design the graph coarsening and employ the sparse constraint to obtain more robust features. We conduct extensive experiments on two widely-used EEG emotion recognition databases, i.e., SJTU emotion EEG dataset (SEED) and Multi-modal physiological emotion recognition dataset (MPED). The results demonstrate that the proposed model achieves the-state-of-the-art performance.

Saving computational budget in Bayesian network-based evolutionary algorithms

During the evolutionary process, algorithms based on probability distributions for generating new individuals suffer from computational burden due to the intensive computation of probability distribution estimations, particularly when using Probabilistic Graph Models (PGMs). In the Bayesian Optimisation Algorithm (BOA), for instance, determining the optimal Bayesian network structure by a given solution sample is an NP-hard problem. To overcome this issue, we consider a new BOA-based optimisation approach (FBOA) which explores the fact that patterns of PGM adjustments can be used as a guide to reduce the frequency of PGM updates because significant changes in PGM structure might not occur so frequently, and because they can be particularly sparse at the end of evolution. In the present paper, this new approach is scrutinised in the search space of an NK-landscape optimisation problem for medium and large-size instances. Average gaps and success rates as well as the correlation between the landscape ruggedness of the problem and the expected runtime of FBOA and BOA are presented for medium-size instances. For large-size instances, optimisation results from FBOA and BOA are compared. The experiments show that, despite our FBOA being of almost three times faster than BOA, it still produces competitive optimisation results.

Technology Hotspot Tracking: Topic Discovery and Evolution of China’s Blockchain Patents Based on a Dynamic LDA Model

Tracking scientific and technological (S&T) research hotspots can help scholars to grasp the status of current research and develop regular patterns in the field over time. It contributes to the generation of new ideas and plays an important role in promoting the writing of scientific research projects and scientific papers. Patents are important S&T resources, which can reflect the development status of the field. In this paper, we use topic modeling, topic intensity, and evolutionary computing models to discover research hotspots and development trends in the field of blockchain patents. First, we propose a time-based dynamic latent Dirichlet allocation (TDLDA) modeling method based on a probabilistic graph model and knowledge representation learning for patent text mining. Second, we present a computational model, topic intensity (TI), that expresses the topic strength and evolution. Finally, the point-wise mutual information (PMI) value is used to evaluate topic quality. We obtain 20 hot topics through TDLDA experiments and rank them according to the strength calculation model. The topic evolution model is used to analyze the topic evolution trend from the perspectives of rising, falling, and stable. From the experiments we found that 8 topics showed an upward trend, 6 topics showed a downward trend, and 6 topics became stable or fluctuated. Compared with the baseline method, TDLDA can have the best effect when K is 40 or less. TDLDA is an effective topic model that can extract hot topics and evolution trends of blockchain patent texts, which helps researchers to more accurately grasp the research direction and improves the quality of project application and paper writing in the blockchain technology domain.

Generating Fake Documents Using Probabilistic Logic Graphs

Past research has shown that over 8 months may elapse between the time when a network is compromised and the time the attack is discovered. During this long gap, attackers can steal valuable intellectual property from the victim. The recent FORGE system [8] has suggested that automatically generating fake—but believable—versions of documents can delay the attacker, cost him money, and increase his uncertainty. However, in order to generate fakes, FORGE only modifies the textual component of the document in question. But in the real world, documents consist of many non-textual components such as charts, equations, formulas, diagrams, and tables. We propose the concept of a Probabilistic Logic Graph (PLG) and show that PLGs provide a single, unified framework within which the different parts of a document can be expressed. We then define the problem of generating, for a given PLG representation of a document, a set of fake yet highly believable PLGs (i.e., documents), so that an attacker looking at them (both the original and the fake ones) cannot easily identify the original document. We show that the problem of generating fake PLGs is intractable—but we propose an approximation algorithm that solves it efficiently. We evaluate the use of PLGs over a corpus of patents and show that our fakes can effectively deceive an adversary.

Optimization-Time Analysis for Cybersecurity

A mathematical framework to reason about time resilience in cybersecurity is here introduced. We first consider an attacker who is able to mount several multi-stage attacks on the organization: the defender’s objective is to select an optimal portfolio of security controls, within a given budget, to withstand the highest number of attacks. The mathematical model is a Markov chain with an initial state called the safe state, intermediate states for all possible attacks (each attack state denoting a probabilistic attack graph), and a sink state denoting a successful attack. The overall defence problem is formulated as a bi-level multi-objective optimization, i.e., the defender selects an optimal portfolio of security controls to mitigate an optimal attacker. In order to determine the probability of success of an attack two cases will be considered: (a) the expected probability of success and (b) the highest probability of success. We refer to these two cases as expected-time analysis and worst-case time analysis, respectively. To solve precisely these bi-level optimizations strong duality and Mixed Integer Linear Programming are used. We then extend the framework to investigate resilience in terms of the total duration of the attacks; variations of the previous optimizations are presented to this purpose. Finally numerical evaluations are provided to compare the results obtained from the expected-time analysis and the worst-case time analysis.

Clustering of graphs using pseudo-guided random walk

Clustering is an unsupervised learning task that models data as coherent groups. Multiple approaches have been proposed in the past to cluster large volumes of data. Graphs provide a logical mapping of many real-world datasets rich enough to reflect various peculiarities of numerous domains. Apart from k-means, k-medoid, and other well-known clustering algorithms, utilization of random walk-based approaches to cluster data is a prominent area of data mining research. Markov clustering algorithm and limited random walk-based clustering are the prominent techniques that utilize the concept of random walk. The main goal of this work is to address the task of clustering graphs using an efficient random walk-based method. A novel walk approach in a graph is presented here that determines the weight of the edges and the degree of the nodes. This information is utilized by the pseudo-guidance model to guide the random walk procedure. This work introduces the friends-of-friends concept during the random walk process so that the edges’ weights are determined utilizing an inclusive criterion. This concept enables a random walk to be initiated from the highest degree node. The random walk continues until the walking agent cannot find any unvisited neighbor(s). The agent walks to its neighbors if it finds a weight of one or more, otherwise the agent’s stopping criteria is met. The nodes visited in this walk form a cluster. Once a walk comes to halt, the visited nodes are removed from the original graph and the next walk starts in the remaining graph. This process continues until all nodes of the graph are traversed. The focus of this work remains random walk-based clustering of graphs. The proposed approach is evaluated using 18 real-world benchmark datasets utilizing six cluster validity indices, namely Davies-Bouldin index (DBI), Dunn index (DI), Silhouette coefficient (SC), Calinski-Harabasz index (CHI), modularity index, and normalized cut. This proposal is compared with seven closely related approaches from the same domain, namely, limited random walk, pairwise clustering, personalized page rank clustering, GAKH (genetic algorithm krill herd) graph clustering, mixing time of random walks, density-based clustering of large probabilistic graphs, and Walktrap. Experiments suggest better performance of this work based on the evaluation metrics.

A graph-based approach to detect unexplained sequences in a log

In this paper we challenge the issue of detecting anomalous events in computer systems log files, through a novel graph mining approach. The basic idea is to model log temporal sequences as a particular graph and event detection as a particular path finding problem. Thus, anomalous sequences correspond to log parts that can not be “explained” by any path in the graph. We propose a novel Iterative Partitioning Log Mining technique to parse any kind of logs and to model their temporal sequence as a probabilistic penalty graph. The approach has been implemented in a framework supporting both real time and batch processing realized on the top of the Apache Spark analytics engine for large-scale data processing. Experimental results show the advantages of the proposed framework in terms of effectiveness for different system configurations.

Graph Neural Network for Robust Public Transit Demand Prediction

Understanding and forecasting mobility patterns and travel demand are fundamental and critical to efficient transport infrastructure planning and service operation. However, most existing studies focused on deterministic demand estimation/prediction/analytics. Differently, this study provides confidence interval based demand forecasting, which can help transport planning and operation authorities to better accommodate demand uncertainty/variability. The proposed Origin-Destination (OD) demand prediction approach well captures and utilizes the correlations among spatial and temporal information. In particular, the proposed Probabilistic Graph Convolution Model (PGCM) consists of two components: (i) a prediction module based on Graph Convolution Network and combined with the gated mechanism to predict OD demand by utilizing spatio-temporal relations; (ii) a Bayesian-based approximation module to measure the confidence interval of demand prediction by evaluating the graph-based model uncertainty. We use a large-scale real-world public transit dataset from the Greater Sydney area to test and evaluate the proposed approach. The experimental results demonstrate that the proposed method is capable of capturing the spatial-temporal correlations for more robust demand prediction against several established tools in the literature.

Probabilistic Graph Models (PGMs) for Feature Selection in Time Series Analysis and Forecasting

Time series or longitudinal analysis has a very important aspect in the field of research. Day by day new and better analyses are getting developed in this field. The main problem of the time series modeling is the presence of heteroskedasticity which was first identified as the autoregressive conditional heteroskedasticitic (ARCH) effect by R. Engle (1969) [15] and then moderated by Bollerslev (1986) [7] in the more generalized form of generalized autoregressive conditional heteroskedasticitic (GARCH) models to explain the conditional dependence and can capture the systematizes evidence of the past variations of the time series variables. And on the other hand, J. Pearl (1985) [33] in the mid-eighties established the use of probabilistic graph models (PGMs), especially it's part where the causation cannot be reversed i.e. directed acyclic graphs (DAGs) as Bayesian Networks (BNs) to determine the conditional independence and is widely used in various fields of life with greater accuracy, precision, and fewer complexities. Fortunately, Bayesian Networks (BNs) are not used to to-date for the analysis of conditional dependencies or conditional independence analysis on the longitudinal data. This paper will review and summarize the uses of GARCH models and the uses of BNs in different fields and the responses of the researchers on the results accuracies and precision in comparison of the other available and applied analyses.