scholarly journals Grus

2021 ◽  
Vol 18 (2) ◽  
pp. 1-25
Author(s):  
Pengyu Wang ◽  
Jing Wang ◽  
Chao Li ◽  
Jianzong Wang ◽  
Haojin Zhu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Data Access ◽  
Structured Data ◽  
Graph Processing ◽  
System Framework ◽  
Large Graphs ◽  
Space Efficiency ◽  
Graph Size ◽  
Optimal Efficiency ◽  
Processing Framework

Today’s GPU graph processing frameworks face scalability and efficiency issues as the graph size exceeds GPU-dedicated memory limit. Although recent GPUs can over-subscribe memory with Unified Memory (UM), they incur significant overhead when handling graph-structured data. In addition, many popular processing frameworks suffer sub-optimal efficiency due to heavy atomic operations when tracking the active vertices. This article presents Grus, a novel system framework that allows GPU graph processing to stay competitive with the ever-growing graph complexity. Grus improves space efficiency through a UM trimming scheme tailored to the data access behaviors of graph workloads. It also uses a lightweight frontier structure to further reduce atomic operations. With easy-to-use interface that abstracts the above details, Grus shows up to 6.4× average speedup over the state-of-the-art in-memory GPU graph processing framework. It allows one to process large graphs of 5.5 billion edges in seconds with a single GPU.

scholarly journals Faster Motif Counting via Succinct Color Coding and Adaptive Sampling

2021 ◽  
Vol 15 (6) ◽  
pp. 1-27
Author(s):  
Marco Bressan ◽  
Stefano Leucci ◽  
Alessandro Panconesi
Keyword(s):  
Adaptive Sampling ◽  
Relative Frequency ◽  
State Of The Art ◽  
Color Coding ◽  
Input Graph ◽  
Large Graphs ◽  
Running Time ◽  
Uniform Sampling ◽  
Current State ◽  
Connected Subgraphs

We address the problem of computing the distribution of induced connected subgraphs, aka graphlets or motifs , in large graphs. The current state-of-the-art algorithms estimate the motif counts via uniform sampling by leveraging the color coding technique by Alon, Yuster, and Zwick. In this work, we extend the applicability of this approach by introducing a set of algorithmic optimizations and techniques that reduce the running time and space usage of color coding and improve the accuracy of the counts. To this end, we first show how to optimize color coding to efficiently build a compact table of a representative subsample of all graphlets in the input graph. For 8-node motifs, we can build such a table in one hour for a graph with 65M nodes and 1.8B edges, which is times larger than the state of the art. We then introduce a novel adaptive sampling scheme that breaks the “additive error barrier” of uniform sampling, guaranteeing multiplicative approximations instead of just additive ones. This allows us to count not only the most frequent motifs, but also extremely rare ones. For instance, on one graph we accurately count nearly 10.000 distinct 8-node motifs whose relative frequency is so small that uniform sampling would literally take centuries to find them. Our results show that color coding is still the most promising approach to scalable motif counting.

VGL: a high-performance graph processing framework for the NEC SX-Aurora TSUBASA vector architecture

2021 ◽  
Author(s):  
Ilya V. Afanasyev ◽  
Vladimir V. Voevodin ◽  
Kazuhiko Komatsu ◽  
Hiroaki Kobayashi
Keyword(s):  
High Performance ◽  
Graph Processing ◽  
Processing Framework

scholarly journals BLADYG: A Graph Processing Framework for Large Dynamic Graphs

2017 ◽  
Vol 9 ◽  
pp. 9-17 ◽  
Author(s):  
Sabeur Aridhi ◽  
Alberto Montresor ◽  
Yannis Velegrakis
Keyword(s):  
Graph Processing ◽  
Dynamic Graphs ◽  
Processing Framework

scholarly journals Multi-View Attribute Graph Convolution Networks for Clustering

2020 ◽  
Author(s):  
Jiafeng Cheng ◽  
Qianqian Wang ◽  
Zhiqiang Tao ◽  
Deyan Xie ◽  
Quanxue Gao
Keyword(s):  
Neural Networks ◽  
State Of The Art ◽  
Graph Embedding ◽  
Structured Data ◽  
Attention Networks ◽  
Graph Data ◽  
Graph Reconstruction ◽  
Node Attributes ◽  
Graph Neural Networks ◽  
Geometric Relationship

Graph neural networks (GNNs) have made considerable achievements in processing graph-structured data. However, existing methods can not allocate learnable weights to different nodes in the neighborhood and lack of robustness on account of neglecting both node attributes and graph reconstruction. Moreover, most of multi-view GNNs mainly focus on the case of multiple graphs, while designing GNNs for solving graph-structured data of multi-view attributes is still under-explored. In this paper, we propose a novel Multi-View Attribute Graph Convolution Networks (MAGCN) model for the clustering task. MAGCN is designed with two-pathway encoders that map graph embedding features and learn the view-consistency information. Specifically, the first pathway develops multi-view attribute graph attention networks to reduce the noise/redundancy and learn the graph embedding features for each multi-view graph data. The second pathway develops consistent embedding encoders to capture the geometric relationship and probability distribution consistency among different views, which adaptively finds a consistent clustering embedding space for multi-view attributes. Experiments on three benchmark graph datasets show the superiority of our method compared with several state-of-the-art algorithms.

scholarly journals Graph Convolutional Neural Network for a Pharmacy Cross-Selling Recommender System

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 525
Author(s):  
Franz Hell ◽  
Yasser Taha ◽  
Gereon Hinz ◽  
Sabine Heibei ◽  
Harald Müller ◽  
...  
Keyword(s):  
Recommender System ◽  
Network Optimization ◽  
Deep Neural Networks ◽  
Product Information ◽  
State Of The Art ◽  
Pharmaceutical Product ◽  
Structured Data ◽  
Graph Structure ◽  
Convolutional Network ◽  
Underlying Graph

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance in recommender system benchmarks. Adapting these methods to pharmacy product cross-selling recommendation tasks with a million products and hundreds of millions of sales remains a challenge, due to the intricate medical and legal properties of pharmaceutical data. To tackle this challenge, we developed a graph convolutional network (GCN) algorithm called PharmaSage, which uses graph convolutions to generate embeddings for pharmacy products, which are then used in a downstream recommendation task. In the underlying graph, we incorporate both cross-sales information from the sales transaction within the graph structure, as well as product information as node features. Via modifications to the sampling involved in the network optimization process, we address a common phenomenon in recommender systems, the so-called popularity bias: popular products are frequently recommended, while less popular items are often neglected and recommended seldomly or not at all. We deployed PharmaSage using real-world sales data and trained it on 700,000 articles represented as nodes in a graph with edges between nodes representing approximately 100 million sales transactions. By exploiting the pharmaceutical product properties, such as their indications, ingredients, and adverse effects, and combining these with large sales histories, we achieved better results than with a purely statistics based approach. To our knowledge, this is the first application of deep graph embeddings for pharmacy product cross-selling recommendation at this scale to date.

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