Compressed Graph Representation Research Articles

GraphScale: Scalable Processing on FPGAs for HBM and Large Graphs

Recent advances in graph processing on FPGAs promise to alleviate performance bottlenecks with irregular memory access patterns. Such bottlenecks challenge performance for a growing number of important application areas like machine learning and data analytics. While FPGAs denote a promising solution through flexible memory hierarchies and massive parallelism, we argue that current graph processing accelerators either use the off-chip memory bandwidth inefficiently or do not scale well across memory channels. In this work, we propose GraphScale, a scalable graph processing framework for FPGAs. GraphScale combines multi-channel memory with asynchronous graph processing (i.e., for fast convergence on results) and a compressed graph representation (i.e., for efficient usage of memory bandwidth and reduced memory footprint). GraphScale solves common graph problems like breadth-first search, PageRank, and weakly connected components through modular user-defined functions, a novel two-dimensional partitioning scheme, and a high-performance two-level crossbar design. Additionally, we extend GraphScale to scale to modern high-bandwidth memory (HBM) and reduce partitioning overhead of large graphs with binary packing.

Context-Aware Auto-Encoded Graph Neural Model for Dynamic Question Generation using NLP

Question generation is an important task in natural language processing that involves generating questions from a given text. This paper proposes a novel approach for dynamic question generation using a context-aware auto-encoded graph neural model. Our approach involves constructing a graph representation of the input text, where each node in the graph corresponds to a word or phrase in the text, and the edges represent the relationships between them. We then use an auto-encoder model to learn a compressed representation of the graph that captures the most important information in the input text. Finally, we use the compressed graph representation to generate questions by dynamically selecting nodes and edges based on their relevance to the context of the input text. We evaluate our approach on four benchmark datasets (SQuAD, Natural Questions, TriviaQA, and QuAC) and demonstrate that it outperforms existing state-of-the-art methods for dynamic question generation. In the experimentation, to evaluate the result four performance metrics are used i.e. BLEU, ROUGE, F1-Score, and Accuracy. The result of the proposed approach yields an accuracy of 92% on the SQuAD dataset, 89% with QuAC, and 84% with TriviaQA. while on the natural questions dataset, the model gives 79% accuracy. Our results suggest that the use of graph neural networks and auto-encoder models can significantly improve the accuracy and effectiveness of question generation in NLP. Further research in this area can lead to even more sophisticated models that can generate questions that are even more contextually relevant and natural-sounding.

Compressed graph representation for scalable molecular graph generation

Recently, deep learning has been successfully applied to molecular graph generation. Nevertheless, mitigating the computational complexity, which increases with the number of nodes in a graph, has been a major challenge. This has hindered the application of deep learning-based molecular graph generation to large molecules with many heavy atoms. In this study, we present a molecular graph compression method to alleviate the complexity while maintaining the capability of generating chemically valid and diverse molecular graphs. We designate six small substructural patterns that are prevalent between two atoms in real-world molecules. These relevant substructures in a molecular graph are then converted to edges by regarding them as additional edge features along with the bond types. This reduces the number of nodes significantly without any information loss. Consequently, a generative model can be constructed in a more efficient and scalable manner with large molecules on a compressed graph representation. We demonstrate the effectiveness of the proposed method for molecules with up to 88 heavy atoms using the GuacaMol benchmark.

A compressed graph representation for services composition

Service-oriented computing enhances business scalability and flexibility. Providers who expect to benefit from it may bring the explosive growth of web services. As a result, the huge storage required in searching a composition solution may be too much to afford. In this paper, we apply a compressed data structure to tackle this challenging issue. We propose a system model to solves web service composition (WSC) problem. In this model, we use a compressed tree to represent the search graph. This work innovatively solve the composition problem with compressed graph representation. The proposed model is verified through theoretical analysis and experimental study. The obtained experimental results show that this system may handle a large number of web services with small space requirement.

A compressed graph representation for services composition

A compressed graph representation for services composition

Numerical integration methods and layout improvements in the context of dynamic RNA visualization

BackgroundRNA visualization software tools have traditionally presented a static visualization of RNA molecules with limited ability for users to interact with the resulting image once it is complete. Only a few tools allowed for dynamic structures. One such tool is jViz.RNA. Currently, jViz.RNA employs a unique method for the creation of the RNA molecule layout by mapping the RNA nucleotides into vertexes in a graph, which we call the detailed graph, and then utilizes a Newtonian mechanics inspired system of forces to calculate a layout for the RNA molecule. The work presented here focuses on improvements to jViz.RNA that allow the drawing of RNA secondary structures according to common drawing conventions, as well as dramatic run-time performance improvements. This is done first by presenting an alternative method for mapping the RNA molecule into a graph, which we call the compressed graph, and then employing advanced numerical integration methods for the compressed graph representation.ResultsComparing the compressed graph and detailed graph implementations, we find that the compressed graph produces results more consistent with RNA drawing conventions. However, we also find that employing the compressed graph method requires a more sophisticated initial layout to produce visualizations that would require minimal user interference. Comparing the two numerical integration methods demonstrates the higher stability of the Backward Euler method, and its resulting ability to handle much larger time steps, a high priority feature for any software which entails user interaction.ConclusionThe work in this manuscript presents the preferred use of compressed graphs to detailed ones, as well as the advantages of employing the Backward Euler method over the Forward Euler method. These improvements produce more stable as well as visually aesthetic representations of the RNA secondary structures. The results presented demonstrate that both the compressed graph representation, as well as the Backward Euler integrator, greatly enhance the run-time performance and usability. The newest iteration of jViz.RNA is available at https://jviz.cs.sfu.ca/download/download.html.

Graph summarization with quality guarantees

We study the problem of graph summarization. Given a large graph we aim at producing a concise lossy representation (a summary) that can be stored in main memory and used to approximately answer queries about the original graph much faster than by using the exact representation. In this work we study a very natural type of summary: the original set of vertices is partitioned into a small number of supernodes connected by superedges to form a complete weighted graph. The superedge weights are the edge densities between vertices in the corresponding supernodes. To quantify the dissimilarity between the original graph and a summary, we adopt the reconstruction error and the cut-norm error. By exposing a connection between graph summarization and geometric clustering problems (i.e., k-means and k-median), we develop the first polynomial-time approximation algorithms to compute the best possible summary of a certain size under both measures. We discuss how to use our summaries to store a (lossy or lossless) compressed graph representation and to approximately answer a large class of queries about the original graph, including adjacency, degree, eigenvector centrality, and triangle and subgraph counting. Using the summary to answer queries is very efficient as the running time to compute the answer depends on the number of supernodes in the summary, rather than the number of nodes in the original graph.

Compact representation of Web graphs with extended functionality

The representation of large subsets of the World Wide Web in the form of a directed graph has been extensively used to analyze structure, behavior, and evolution of those so-called Web graphs. However, interesting Web graphs are very large and their classical representations do not fit into the main memory of typical computers, whereas the required graph algorithms perform inefficiently on secondary memory. Compressed graph representations drastically reduce their space requirements while allowing their efficient navigation in compressed form. While the most basic navigation operation is to retrieve the successors of a node, several important Web graph algorithms require support for extended queries, such as finding the predecessors of a node, checking the presence of a link, or retrieving links between ranges of nodes. Those are seldom supported by compressed graph representations.This paper presents the k2-tree, a novel Web graph representation based on a compact tree structure that takes advantage of large empty areas of the adjacency matrix of the graph. The representation not only retrieves successors and predecessors in symmetric fashion, but also it is particularly efficient to check for specific links between nodes, or between ranges of nodes, or to list the links between ranges. Compared to the best representations in the literature supporting successor and predecessor queries, our technique offers the least space usage (1–3 bits per link) while supporting fast navigation to predecessors and successors (2–8μs per neighbor retrieved) and sharply outperforming the others on the extended queries. The representation is also of general interest and can be used to compress other kinds of graphs and data structures.

Fast and Compact Web Graph Representations

Compressed graph representations, in particular for Web graphs, have become an attractive research topic because of their applications in the manipulation of huge graphs in main memory. The state of the art is well represented by the WebGraph project, where advantage is taken of several particular properties of Web graphs to offer a trade-off between space and access time. In this paper we show that the same properties can be exploited with a different and elegant technique that builds on grammar-based compression. In particular, we focus on Re-Pair and on Ziv-Lempel compression, which, although cannot reach the best compression ratios of WebGraph, achieve much faster navigation of the graph when both are tuned to use the same space. Moreover, the technique adapts well to run on secondary memory and in distributed scenarios. As a byproduct, we introduce an approximate Re-Pair version that works efficiently with severely limited main memory.

A representation for the Mexican political networks

A compressed graph representation for use with the Mexican political networks is introduced. Properties of these graphs are investigated. It is also explained how the Jorge–Schmidt power centrality index can be used to index the centrality of nodes in the original network from the compressed graph representation.