Graph Computation Research Articles

COVID-19 Knowledge Graph from semantic integration of biomedical literature and databases.

SummaryThe global response to the COVID-19 pandemic has led to a rapid increase of scientific literature on this deadly disease. Extracting knowledge from biomedical literature and integrating it with relevant information from curated biological databases is essential to gain insight into COVID-19 etiology, diagnosis and treatment. We used Semantic Web technology RDF to integrate COVID-19 knowledge mined from literature by iTextMine, PubTator and SemRep with relevant biological databases and formalized the knowledge in a standardized and computable COVID-19 Knowledge Graph (KG). We published the COVID-19 KG via a SPARQL endpoint to support federated queries on the Semantic Web and developed a knowledge portal with browsing and searching interfaces. We also developed a RESTful API to support programmatic access and provided RDF dumps for download.Availability and implementationThe COVID-19 Knowledge Graph is publicly available under CC-BY 4.0 license at https://research.bioinformatics.udel.edu/covid19kg/.Supplementary information Supplementary data are available at Bioinformatics online.

Strudel: A fast and accurate learner of structured-decomposable probabilistic circuits

Probabilistic circuits (PCs) represent a probability distribution as a computational graph. Enforcing structural properties on these graphs guarantees that several inference scenarios become tractable. Among these properties, structured decomposability is a particularly appealing one: it enables the efficient and exact computations of the probability of complex logical formulas, and can be used to reason about the expected output of certain predictive models under missing data. This paper proposes Strudel, a simple, fast and accurate learning algorithm for structured-decomposable PCs. Compared to prior work for learning structured-decomposable PCs, Strudel delivers more accurate single PC models in fewer iterations, and dramatically scales learning when building ensembles of PCs. It achieves this scalability by exploiting another structural property of PCs, called determinism, and by sharing the same computational graph across mixture components. We show these advantages on standard density estimation benchmarks and challenging inference scenarios.

Verifying BQP Computations on Noisy Devices with Minimal Overhead

Recent experimental achievements motivate an ever-growing interest from companies starting to feel the limitations of classical computing. Yet, in light of ongoing privacy scandals, the future availability of quantum computing through remotely accessible servers pose peculiar challenges: Clients with quantum-limited capabilities want their data and algorithms to remain hidden, while being able to verify that their computations are performed correctly. Research in blind and verifiable delegation of quantum computing attempts to address this question. However, available techniques suffer not only from high overheads but also from over-sensitivity: When running on noisy devices, imperfections trigger the same detection mechanisms as malicious attacks, resulting in perpetually aborted computations. Hence, while malicious quantum computers are rendered harmless by blind and verifiable protocols, inherent noise severely limits their usability. We address this problem with an efficient, robust, blind, verifiable scheme to delegate deterministic quantum computations with classical inputs and outputs. We show that: 1) a malicious Server can cheat at most with an exponentially small success probability; 2) in case of sufficiently small noise, the protocol succeeds with a probability exponentially close to 1; 3) the overhead is barely a polynomial number of repetitions of the initial computation interleaved with test runs requiring the same physical resources in terms of memory and gates; 4) the amount of tolerable noise, measured by the probability of failing a test run, can be as high as 25% for some computations and will be generally bounded by 12.5% when using a planar graph resource state. The key points are that security can be provided without universal computation graphs and that, in our setting, full fault-tolerance is not needed to amplify the confidence level exponentially close to 1.

Performance and Accuracy Tradeoffs for Training Graph Neural Networks on ReRAM-Based Architectures

Graph neural network (GNN) is a variant of deep neural networks (DNNs) operating on graphs. However, GNNs are more complex compared with DNNs as they simultaneously exhibit attributes of both DNN and graph computations. In this work, we propose a ReRAM-based 3-D manycore processing-in-memory architecture called ReMaGN, tailored for on-chip training of GNNs. ReMaGN implements GNN training using reduced-precision representation to make the computation faster and reduce the load on the communication backbone. However, reduced precision can potentially compromise the accuracy of training. Hence, we undertake a study of performance and accuracy tradeoffs in such architectures. We demonstrate that ReMaGN outperforms conventional GPUs by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.5\times $ </tex-math></inline-formula> (on average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.1\times $ </tex-math></inline-formula> ) in terms of execution time, while being up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$42\times $ </tex-math></inline-formula> (on average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$33.5\times $ </tex-math></inline-formula> ) more energy efficient without sacrificing accuracy.

SOOP: Efficient Distributed Graph Computation Supporting Second-Order Random Walks

SOOP: Efficient Distributed Graph Computation Supporting Second-Order Random Walks

On the threshold of intractability

The computational complexity of the graph modification problems Threshold Editing and Chain Editing has been an important open question in computational graph theory for more than 15 years. These problems consist of adding and deleting the fewest possible edges to transform a graph into a threshold or chain graph. We show that both problems are NP-hard, resolving a conjecture by Natanzon, Shamir, and Sharan from 2001. On the positive side, we show both problems admit quadratic vertex kernels and give subexponential time parameterized algorithms solving both problems in 2O(klog⁡k)+nO(1) time. Few natural problems are known to be in this complexity class. These results also extend to the completion and deletion variants of both problems, and threshold/chain graphs are the only known graph classes for which all three versions—completion, deletion, and editing—are both NP-complete and solvable in subexponential parameterized time.

Local Graph Edge Partitioning

Graph edge partitioning, which is essential for the efficiency of distributed graph computation systems, divides a graph into several balanced partitions within a given size to minimize the number of vertices to be cut. Existing graph partitioning models can be classified into two categories: offline and streaming graph partitioning models. The former requires global graph information during the partitioning, which is expensive in terms of time and memory for large-scale graphs. The latter creates partitions based solely on the received graph information. However, the streaming model may result in a lower partitioning quality compared with the offline model. Therefore, this study introduces a Local Graph Edge Partitioning model, which considers only the local information (i.e., a portion of a graph instead of the entire graph) during the partitioning. Considering only the local graph information is meaningful because acquiring complete information for large-scale graphs is expensive. Based on the Local Graph Edge Partitioning model, two local graph edge partitioning algorithms—Two-stage Local Partitioning and Adaptive Local Partitioning—are given. Experimental results obtained on 14 real-world graphs demonstrate that the proposed algorithms outperform rival algorithms in most tested cases. Furthermore, the proposed algorithms are proven to significantly improve the efficiency of the real graph computation system GraphX.

Semi-Supervised Noisy Label Learning for Chinese Clinical Named Entity Recognition

This paper describes our approach for the Chinese clinical named entity recognition (CNER) task organized by the 2020 China Conference on Knowledge Graph and Semantic Computing (CCKS) competition. In this task, we need to identify the entity boundary and category labels of six entities from Chinese electronic medical record (EMR). We constructed a hybrid system composed of a semi-supervised noisy label learning model based on adversarial training and a rule post-processing module. The core idea of the hybrid system is to reduce the impact of data noise by optimizing the model results. Besides, we used post-processing rules to correct three cases of redundant labeling, missing labeling, and wrong labeling in the model prediction results. Our method proposed in this paper achieved strict criteria of 0.9156 and relax criteria of 0.9660 on the final test set, ranking first.

Transistor Compact Model Based on Multigradient Neural Network and Its Application in SPICE Circuit Simulations for Gate-All-Around Si Cold Source FETs

Transistor compact model (TCM) is the key bridge between process technology and circuit design. Typically, TCM is desired to capture the nonlinear device electronic characteristics and their high-order derivatives. However, for the novel devices in advanced and future technologies, establishing TCM based on analytical equations and extracting model parameters becomes tedious. The model fitting capability for device outputs’ high-order derivatives is also limited. These drawbacks hinder fast and accurate device to circuit evaluation cycles. We develop a TCM based on multigradient neural network (MNN) using computational graph in the PyTorch framework. This MNN model is able to simultaneously capture the transistor dc/ac characteristics, such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${I}-{V}/{Q}-{V}$ </tex-math></inline-formula> , their derivatives ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${G}-{V}/{C}-{V}$ </tex-math></inline-formula> ), and higher order derivatives accurately. Moreover, the model architecture can be widely adapted to various device types. Based on this model scheme, software is developed to enable the automated model generation for standard SPICE simulation. Finally, the model and software are validated for novel gate-all-around (GAA) Si cold source field-effect transistors (CSFET), and 19-stage ring oscillator and two-stage operational amplifier circuit simulations have also been demonstrated. This work reduces the cycle of novel device compact model creation and circuit benchmark simulation from months or weeks to hours. In addition, it enables more precise circuit simulation for analog and RF circuits, and it provides a rapid solution for early stage design technology cooptimization (DTCO).

Nonlinear vibration analysis of forced response for rubbing problems using the automatic differential frame

The multi-harmonic balance method is widely applied to obtain the forced responses of nonlinear systems undergoing rubbing problems. Despite large-scale time savings compared with the time marching method, it suffers from the complicated derivations of the Jacobian matrix. To solve this problem, this paper focuses on applying the automatic differentiation frame to the multi-harmonic balance method to implement the nonlinear vibration analysis of systems subjected to the rub phenomena. By establishing computational graph and utilizing the automatic differentiation process, tedious works such as the derivations of the complicated analytical expressions of the Jacobian matrix are avoided, which guarantees the efficiency and applicability of the presented method. A single-degree-of-freedom system with nonlinear force in the form of cubic is used to verify the accuracy of the method, and numerical analysis results reveal that the method is accurate enough compared with the time marching method. Furthermore, for the purpose of application, the responses of two common friction models, which are of great concern in some practical engineering fields, including a two-degree-of-freedom system containing a friction damper and a rotor disk system with circumferential rubbing, are obtained utilizing the presented approach. The influences of several model parameters on their responses are investigated as well. Numerical investigations demonstrate that the automatic differential solution framework developed in this research for solving nonlinear vibration equations has high accuracy and eliminates the need for a complicated partial derivative analytical formula derivation.

A Universal Simulation Framework of Shipborne Inertial Sensors Based on the Ship Motion Model and Robot Operating System

A complete virtual test environment is a powerful tool for Autonomous Surface Vessels (ASVs) research, and the simulation of ship motion and shipborne sensors is one of the prerequisites for constructing such an environment. This paper proposed a universal simulation framework of shipborne inertial sensors. A ship motion model considering environmental disturbances is proposed to simulate the six-degrees-of-freedom motion of ships. The discrete form of the inertial sensor stochastic error model is derived. The inertial measurement data are simulated by adding artificial errors to a simulated motion status. In addition, the ship motion simulation, inertial measurement simulation, and environment simulation nodes are implemented based on the computational graph architecture of the Robot Operating System (ROS). The benefit from the versatility of the ROS messages, the format of simulated inertial measurement is exactly the same as that of real sensors, which provides a research basis for the fusion perception algorithm based on visual–inertial and laser–inertial sensors in the research field of ASVs.

Using Technology With Future Teachers Of Primary School Mathematics: A Glimpse Into An American Experience

The paper describes the author’s experience in using technology within mathematics content and methods (undergraduate and graduate) courses for prospective teachers of primary grades (age 5–10). The main pedagogical idea behind the courses is to change pre-teachers’ perception of mathematics as a subject matter most people predictably dislike. It is suggested that technology can assist instructors in making mathematics an enjoyable subject matter without sacrificing content. The paper provides examples of using Excel, Wolfram Alpha, dynamic geometry software, computer graphing program, and the Online Encyclopedia of Integer Sequences. In conclusion, solicited comments by teacher candidates about their experience of learning computer assisted mathematics are shared.

Deep-freeze graph training for latent learning

Scientific and engineering advances are primarily driven by multi-tier conceptual constructs and conditional theoretical frameworks. The theories allow predictions of hypothetical system responses, given a set of approximate conditions (ranges of applicability) imposed on latent parameters that cannot be measured directly. Learning to estimate the latent variables (Latent Learning) helps to pinpoint the anticipated range-edge anomalies and improves the confidence in interpretation, interpolation and extrapolation of limited experimental data. Due to high dimensionality and extreme non-linearity of the materials science problems, very large datasets are typically required for conventional data-driven model development. The vital experimental data collection, particularly on microstructural phases, is very challenging, which makes it difficult to compile a high-quality database. Incorporation of the domain knowledge into the computational graph structure, initialization and optimization processes presents a viable mechanism for developing accurate models, with limited datasets. This study successfully utilized the approach to build the Deep Freeze Graph (DeepFreG) by mapping known causality relationships and by digitizing empirical domain knowledge for Latent Learning (LL), with specific applications in materials science.

A simulation study investigating potential diffusion-based MRI signatures of microstrokes

Recent studies suggested that cerebrovascular micro-occlusions, i.e. microstokes, could lead to ischemic tissue infarctions and cognitive deficits. Due to their small size, identifying measurable biomarkers of these microvascular lesions remains a major challenge. This work aims to simulate potential MRI signatures combining arterial spin labeling (ASL) and multi-directional diffusion-weighted imaging (DWI). Driving our hypothesis are recent observations demonstrating a radial reorientation of microvasculature around the micro-infarction locus during recovery in mice. Synthetic capillary beds, randomly- and radially-oriented, and optical coherence tomography (OCT) angiograms, acquired in the barrel cortex of mice (n = 5) before and after inducing targeted photothrombosis, were analyzed. Computational vascular graphs combined with a 3D Monte-Carlo simulator were used to characterize the magnetic resonance (MR) response, encompassing the effects of magnetic field perturbations caused by deoxyhemoglobin, and the advection and diffusion of the nuclear spins. We quantified the minimal intravoxel signal loss ratio when applying multiple gradient directions, at varying sequence parameters with and without ASL. With ASL, our results demonstrate a significant difference (p < 0.05) between the signal-ratios computed at baseline and 3 weeks after photothrombosis. The statistical power further increased (p < 0.005) using angiograms measured at week 4. Without ASL, no reliable signal change was found. We found that higher ratios, and accordingly improved significance, were achieved at lower magnetic field strengths (e.g., B0 = 3T) and shorter echo time TE (< 16 ms). Our simulations suggest that microstrokes might be characterized through ASL-DWI sequence, providing necessary insights for posterior experimental validations, and ultimately, future translational trials.

AgFlow: fast model selection of penalized PCA via implicit regularization effects of gradient flow

Principal component analysis (PCA) has been widely used as an effective technique for feature extraction and dimension reduction. In the High Dimension Low Sample Size setting, one may prefer modified principal components, with penalized loadings, and automated penalty selection by implementing model selection among these different models with varying penalties. The earlier work (Zou et al. in J Comput Graph Stat 15(2):265–286, 2006; Gaynanova et al. in J Comput Graph Stat 26(2):379–387, 2017) has proposed penalized PCA, indicating the feasibility of model selection in \(\ell _2\)-penalized PCA through the solution path of Ridge regression, however, it is extremely time-consuming because of the intensive calculation of matrix inverse. In this paper, we propose a fast model selection method for penalized PCA, named approximated gradient flow (AgFlow), which lowers the computation complexity through incorporating the implicit regularization effect introduced by (stochastic) gradient flow (Ali et al. in: The 22nd international conference on artificial intelligence and statistics, pp 1370–1378, 2019; Ali et al. in: International conference on machine learning, 2020) and obtains the complete solution path of \(\ell _2\)-penalized PCA under varying \(\ell _2\)-regularization. We perform extensive experiments on real-world datasets. AgFlow outperforms existing methods (Oja and Karhunen in J Math Anal Appl 106(1):69–84, 1985; Hardt and Price in: Advances in neural information processing systems, pp 2861–2869, 2014; Shamir in: International conference on machine learning, pp 144–152, PMLR, 2015; and the vanilla Ridge estimators) in terms of computation costs.

GraphScope

GraphScope is a system and a set of language extensions that enable a new programming interface for large-scale distributed graph computing. It generalizes previous graph processing frameworks (e.g. , Pregel, GraphX) and distributed graph databases ( e.g ., Janus-Graph, Neptune) in two important ways: by exposing a unified programming interface to a wide variety of graph computations such as graph traversal, pattern matching, iterative algorithms and graph neural networks within a high-level programming language; and by supporting the seamless integration of a highly optimized graph engine in a general purpose data-parallel computing system. A GraphScope program is a sequential program composed of declarative data-parallel operators, and can be written using standard Python development tools. The system automatically handles the parallelization and distributed execution of programs on a cluster of machines. It outperforms current state-of-the-art systems by enabling a separate optimization (or family of optimizations) for each graph operation in one carefully designed coherent framework. We describe the design and implementation of GraphScope and evaluate system performance using several real-world applications.

Bin2vec: learning representations of binary executable programs for security tasks

Tackling binary program analysis problems has traditionally implied manually defining rules and heuristics, a tedious and time consuming task for human analysts. In order to improve automation and scalability, we propose an alternative direction based on distributed representations of binary programs with applicability to a number of downstream tasks. We introduce Bin2vec, a new approach leveraging Graph Convolutional Networks (GCN) along with computational program graphs in order to learn a high dimensional representation of binary executable programs. We demonstrate the versatility of this approach by using our representations to solve two semantically different binary analysis tasks – functional algorithm classification and vulnerability discovery. We compare the proposed approach to our own strong baseline as well as published results, and demonstrate improvement over state-of-the-art methods for both tasks. We evaluated Bin2vec on 49191 binaries for the functional algorithm classification task, and on 30 different CWE-IDs including at least 100 CVE entries each for the vulnerability discovery task. We set a new state-of-the-art result by reducing the classification error by 40% compared to the source-code based inst2vec approach, while working on binary code. For almost every vulnerability class in our dataset, our prediction accuracy is over 80% (and over 90% in multiple classes).

GraphScope

Due to diverse graph data and algorithms, programming and orchestration of complex computation pipelines have become the major challenges to making use of graph applications for Web-scale data analysis. GraphScope aims to provide a one-stop and efficient solution for a wide range of graph computations at scale. It extends previous systems by offering a unified and high-level programming interface and allowing the seamless integration of specialized graph engines in a general data-parallel computing environment. As we will show in this demo, GraphScope enables developers to write sequential graph programs in Python and provides automatic parallel execution on a cluster. This further allows GraphScope to seamlessly integrate with existing data processing systems in PyData ecosystem. To validate GraphScope's efficiency, we will compare a complex, multi-staged processing pipeline for a real-life fraud detection task with a manually assembled implementation comprising multiple systems. GraphScope achieves a 2.86× speedup on a trillion-scale graph in real production at Alibaba.

On the Distributed Complexity of Large-Scale Graph Computations

Motivated by the increasing need to understand the distributed algorithmic foundations of large-scale graph computations, we study some fundamental graph problems in a message-passing model for distributed computing where k ≥ 2 machines jointly perform computations on graphs with n nodes (typically, n &gt;&gt; k). The input graph is assumed to be initially randomly partitioned among the k machines, a common implementation in many real-world systems. Communication is point-to-point, and the goal is to minimize the number of communication rounds of the computation. Our main contribution is the General Lower Bound Theorem , a theorem that can be used to show non-trivial lower bounds on the round complexity of distributed large-scale data computations. This result is established via an information-theoretic approach that relates the round complexity to the minimal amount of information required by machines to solve the problem. Our approach is generic, and this theorem can be used in a “cookbook” fashion to show distributed lower bounds for several problems, including non-graph problems. We present two applications by showing (almost) tight lower bounds on the round complexity of two fundamental graph problems, namely, PageRank computation and triangle enumeration . These applications show that our approach can yield lower bounds for problems where the application of communication complexity techniques seems not obvious or gives weak bounds, including and especially under a stochastic partition of the input. We then present distributed algorithms for PageRank and triangle enumeration with a round complexity that (almost) matches the respective lower bounds; these algorithms exhibit a round complexity that scales superlinearly in k , improving significantly over previous results [Klauck et al., SODA 2015]. Specifically, we show the following results: PageRank: We show a lower bound of Ὼ(n/k 2 ) rounds and present a distributed algorithm that computes an approximation of the PageRank of all the nodes of a graph in Õ(n/k 2 ) rounds. Triangle enumeration: We show that there exist graphs with m edges where any distributed algorithm requires Ὼ(m/k 5/3 ) rounds. This result also implies the first non-trivial lower bound of Ὼ(n 1/3 ) rounds for the congested clique model, which is tight up to logarithmic factors. We then present a distributed algorithm that enumerates all the triangles of a graph in Õ(m/k 5/3 + n/k 4/3 ) rounds.

DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries

Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. Our approach abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly conduct research at scale. We demonstrate the utility of these tools by searching for motifs on simulated data and real public connectomics datasets, and we share simple and complex structures relevant to the neuroscience community. We contextualize our findings and provide case studies and software to motivate future neuroscience exploration.