Fast GPU-Based Generation of Large Graph Networks From Degree Distributions

Synthetically generated, large graph networks serve as useful proxies to real-world networks for many graph-based applications. The ability to generate such networks helps overcome several limitations of real-world networks regarding their number, availability, and access. Here, we present the design, implementation, and performance study of a novel network generator that can produce very large graph networks conforming to any desired degree distribution. The generator is designed and implemented for efficient execution on modern graphics processing units (GPUs). Given an array of desired vertex degrees and number of vertices for each desired degree, our algorithm generates the edges of a random graph that satisfies the input degree distribution. Multiple runtime variants are implemented and tested: 1) a uniform static work assignment using a fixed thread launch scheme, 2) a load-balanced static work assignment also with fixed thread launch but with cost-aware task-to-thread mapping, and 3) a dynamic scheme with multiple GPU kernels asynchronously launched from the CPU. The generation is tested on a range of popular networks such as Twitter and Facebook, representing different scales and skews in degree distributions. Results show that, using our algorithm on a single modern GPU (NVIDIA Volta V100), it is possible to generate large-scale graph networks at rates exceeding 50 billion edges per second for a 69 billion-edge network. GPU profiling confirms high utilization and low branching divergence of our implementation from small to large network sizes. For networks with scattered distributions, we provide a coarsening method that further increases the GPU-based generation speed by up to a factor of 4 on tested input networks with over 45 billion edges.

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1588-P: Therapy Trends in Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽

10.2337/db20-1588-p ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 1588-P ◽

Cited By ~ 1

Author(s):

ROMIK GHOSH ◽

ASHOK K. DAS ◽

AMBRISH MITHAL ◽

SHASHANK JOSHI ◽

K.M. PRASANNA KUMAR ◽

...

Keyword(s):

Real World ◽

Large Scale ◽

Nationwide Study

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2258-PUB: Impact of Cardiovascular (CV) Risk Factors during the Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽

10.2337/db20-2258-pub ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 2258-PUB

Author(s):

ROMIK GHOSH ◽

ASHOK K. DAS ◽

SHASHANK JOSHI ◽

AMBRISH MITHAL ◽

K.M. PRASANNA KUMAR ◽

...

Keyword(s):

Risk Factors ◽

Real World ◽

Large Scale ◽

Nationwide Study

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Models of network formation

10.1093/oso/9780198805090.003.0013 ◽

2018 ◽

Author(s):

Mark Newman

Keyword(s):

Degree Distribution ◽

Network Formation ◽

Preferential Attachment ◽

Network Size ◽

Large Network ◽

Citation Networks ◽

Airline Networks ◽

Attachment Model ◽

Delivery Networks

This chapter describes models of the growth or formation of networks, with a particular focus on preferential attachment models. It starts with a discussion of the classic preferential attachment model for citation networks introduced by Price, including a complete derivation of the degree distribution in the limit of large network size. Subsequent sections introduce the Barabasi-Albert model and various generalized preferential attachment models, including models with addition or removal of extra nodes or edges and models with nonlinear preferential attachment. Also discussed are node copying models and models in which networks are formed by optimization processes, such as delivery networks or airline networks.

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The graph neural networking challenge

ACM SIGCOMM Computer Communication Review ◽

10.1145/3477482.3477485 ◽

2021 ◽

Vol 51 (3) ◽

pp. 9-16

Author(s):

José Suárez-Varela ◽

Miquel Ferriol-Galmés ◽

Albert López ◽

Paul Almasan ◽

Guillermo Bernárdez ◽

...

Keyword(s):

Machine Learning ◽

Computer Networks ◽

Real World ◽

Large Scale ◽

Lessons Learned ◽

Educational Resources ◽

Global Competition ◽

International Telecommunication Union ◽

International Telecommunication ◽

Broad Audience

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

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Prevention of Mountain Disasters and Maintenance of Residential Area through Real-Time Terrain Rendering

Sustainability ◽

10.3390/su13052950 ◽

2021 ◽

Vol 13 (5) ◽

pp. 2950

Author(s):

Su-Kyung Sung ◽

Eun-Seok Lee ◽

Byeong-Seok Shin

Keyword(s):

Real Time ◽

Graphics Processing Units ◽

High Speed ◽

Large Scale ◽

Civil Engineering ◽

Three Dimensional ◽

Sampled Data ◽

Residential Areas ◽

Terrain Rendering ◽

Mountain Disasters

Climate change increases the frequency of localized heavy rains and typhoons. As a result, mountain disasters, such as landslides and earthworks, continue to occur, causing damage to roads and residential areas downstream. Moreover, large-scale civil engineering works, including dam construction, cause rapid changes in the terrain, which harm the stability of residential areas. Disasters, such as landslides and earthenware, occur extensively, and there are limitations in the field of investigation; thus, there are many studies being conducted to model terrain geometrically and to observe changes in terrain according to external factors. However, conventional topography methods are expressed in a way that can only be interpreted by people with specialized knowledge. Therefore, there is a lack of consideration for three-dimensional visualization that helps non-experts understand. We need a way to express changes in terrain in real time and to make it intuitive for non-experts to understand. In conventional height-based terrain modeling and simulation, there is a problem in which some of the sampled data are irregularly distorted and do not show the exact terrain shape. The proposed method utilizes a hierarchical vertex cohesion map to correct inaccurately modeled terrain caused by uniform height sampling, and to compensate for geometric errors using Hausdorff distances, while not considering only the elevation difference of the terrain. The mesh reconstruction, which triangulates the three-vertex placed at each location and makes it the smallest unit of 3D model data, can be done at high speed on graphics processing units (GPUs). Our experiments confirm that it is possible to express changes in terrain accurately and quickly compared with existing methods. These functions can improve the sustainability of residential spaces by predicting the damage caused by mountainous disasters or civil engineering works around the city and make it easy for non-experts to understand.

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A Heuristic Algorithm for Solving a Large-scale Real-world Territory Design Problem

Omega ◽

10.1016/j.omega.2021.102442 ◽

2021 ◽

pp. 102442

Author(s):

Lin Zhou ◽

Lu Zhen ◽

Roberto Baldacci ◽

Marco Boschetti ◽

Ying Dai ◽

...

Keyword(s):

Heuristic Algorithm ◽

Real World ◽

Design Problem ◽

Large Scale ◽

Territory Design

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An information theoretic approach to link prediction in multiplex networks

Scientific Reports ◽

10.1038/s41598-021-92427-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Seyed Hossein Jafari ◽

Amir Mahdi Abdolhosseini-Qomi ◽

Masoud Asadpour ◽

Maseud Rahgozar ◽

Naser Yazdani

Keyword(s):

Real World ◽

Link Prediction ◽

Large Scale ◽

Similarity Measures ◽

Prediction Method ◽

General Purpose ◽

Fast Method ◽

Theoretic Approach ◽

Multiplex Networks ◽

Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

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Monoclonal Antibodies Targeting CGRP: From Clinical Studies to Real-World Evidence—What Do We Know So Far?

Pharmaceuticals ◽

10.3390/ph14070700 ◽

2021 ◽

Vol 14 (7) ◽

pp. 700

Author(s):

Theodoros Mavridis ◽

Christina I. Deligianni ◽

Georgios Karagiorgis ◽

Ariadne Daponte ◽

Marianthi Breza ◽

...

Keyword(s):

Monoclonal Antibodies ◽

Real World ◽

Large Scale ◽

Randomized Clinical Trials ◽

Clinical Investigation ◽

Phase Iii ◽

Real World Evidence ◽

Symptomatic Management ◽

Repurposed Drugs ◽

Cephalic Pain

Now more than ever is the time of monoclonal antibody use in neurology. In headaches, disease-specific and mechanism-based treatments existed only for symptomatic management of migraines (i.e., triptans), while the standard prophylactic anti-migraine treatments consist of non-specific and repurposed drugs that share limited safety profiles and high risk for interactions with other medications, resulting in rundown adherence rates. Recent advances in headache science have increased our understanding of the role of calcitonin gene relate peptide (CGRP) and pituitary adenylate cyclase-activating polypeptide (PACAP) pathways in cephalic pain neurotransmission and peripheral or central sensitization, leading to the development of monoclonal antibodies (mAbs) or small molecules targeting these neuropeptides or their receptors. Large scale randomized clinical trials confirmed that inhibition of the CGRP system attenuates migraine, while the PACAP mediated nociception is still under scientific and clinical investigation. In this review, we provide the latest clinical evidence for the use of anti-CGRP in migraine prevention with emphasis on efficacy and safety outcomes from Phase III and real-world studies.

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Extrinsic Camera Calibration with Line-Laser Projection

Sensors ◽

10.3390/s21041091 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1091

Author(s):

Izaak Van Crombrugge ◽

Rudi Penne ◽

Steve Vanlanduit

Keyword(s):

Camera Calibration ◽

Real World ◽

Large Scale ◽

State Of The Art ◽

Bundle Adjustment ◽

Field Of View ◽

Extrinsic Calibration ◽

Practical Procedure ◽

Partial Overlap

Knowledge of precise camera poses is vital for multi-camera setups. Camera intrinsics can be obtained for each camera separately in lab conditions. For fixed multi-camera setups, the extrinsic calibration can only be done in situ. Usually, some markers are used, like checkerboards, requiring some level of overlap between cameras. In this work, we propose a method for cases with little or no overlap. Laser lines are projected on a plane (e.g., floor or wall) using a laser line projector. The pose of the plane and cameras is then optimized using bundle adjustment to match the lines seen by the cameras. To find the extrinsic calibration, only a partial overlap between the laser lines and the field of view of the cameras is needed. Real-world experiments were conducted both with and without overlapping fields of view, resulting in rotation errors below 0.5°. We show that the accuracy is comparable to other state-of-the-art methods while offering a more practical procedure. The method can also be used in large-scale applications and can be fully automated.

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