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Finding distance-preserving subgraphs in large road networks

Da Yan, J. Cheng, W. Ng, S. Liu
2013 2013 IEEE 29th International Conference on Data Engineering (ICDE)  
Given two sets of points, S and T , in a road network, G, a distance-preserving subgraph (DPS) query returns a subgraph of G that preserves the shortest path from any point in S to any point in T .  ...  In this paper, we study efficient algorithms for processing DPS queries in large road networks.  ...  INTRODUCTION Given two point sets S and T in a road network G, a distance-preserving subgraph (DPS) query returns a subgraph of G that preserves the shortest path distance between any two points s ∈ S  ... 
doi:10.1109/icde.2013.6544861 dblp:conf/icde/YanCNL13 fatcat:xcetjqqxjjfhjc2yoznyu5wm7e

A Multiview Representation Learning Framework for Large-Scale Urban Road Networks

Kaiqi Chen, Guowei Chu, Kaiyuan Lei, Yan Shi, Min Deng
2022 Applied Sciences  
In this process, a large-scale road network organization method was established to improve the random walk algorithm efficiency.  ...  Consequently, we proposed a novel multiview representation learning framework for large-scale urban road networks to simultaneously preserve topological and human mobility information.  ...  Acknowledgments: This work was conducted in part using computing resources at the High-Performance Computing Platform of the Central South University.  ... 
doi:10.3390/app12136301 fatcat:5kx24pyt5jfeplajkrbpvevuvu

Approximate Computation for Big Data Analytics [article]

Shuai Ma, Jinpeng Huai
2019 arXiv   pre-print
and dense subgraph computation.  ...  , network anomaly detection and link prediction.  ...  This work is supported in part by 973 program (2014CB340300), NSFC (U1636210 & 61421003).  ... 
arXiv:1901.00232v1 fatcat:mxbutgrtmrf7zaogjuaqriyq6u

Hub-Accelerator: Fast and Exact Shortest Path Computation in Large Social Networks [article]

Ruoming Jin, Ning Ruan, Bo You, Haixun Wang
2013 arXiv   pre-print
Though existing techniques are quite effective for finding the shortest path on large but sparse road networks, social graphs have quite different characteristics: they are generally non-spatial, non-weighted  ...  These techniques enable us to significantly reduce the search space by either greatly limiting the expansion scope of hubs (using the novel distance- preserving Hub-Network concept) or completely pruning  ...  Finding the minimal distance-preserving subgraph of a collection D of vertex pairs in a graph is an NP-hard problem.  ... 
arXiv:1305.0507v1 fatcat:w3noaal4tfc3rbv6k4piwgymea

Keyword Search on Large Graphs: A Survey

Jianye Yang, Wu Yao, Wenjie Zhang
2021 Data Science and Engineering  
Essentially, given a graph G and query Q associated with a set of keywords, the keyword search aims to find a substructure (e.g., rooted tree or subgraph) S in G such that nodes in S collectively cover  ...  AbstractWith the prevalence of Internet access and online services, various big graphs are generated in many real applications (e.g., online social networks and knowledge graphs).  ...  Keyword Routing on Road Networks In a road network, a route is a path such that it goes through a sequence of vertices following the relevant edges in the road network.  ... 
doi:10.1007/s41019-021-00154-4 fatcat:qo5ikgtj2zhetbhenljyujxbli

Efficient geometric graph matching using vertex embedding

Ayser Armiti, Michael Gertz
2013 Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - SIGSPATIAL'13  
For many applications such as road network analysis and image processing, it is critical to study spatial properties of objects in addition to object relationships.  ...  Initially, a spatial feature is extracted from each vertex, and string edit distance is used to find the distance between pairs of vertices.  ...  Given a road network and a geometric graph that is generated by a GPS-enabled device, locate that device in the road network. 2. Find differences in a road network over time. 3 .  ... 
doi:10.1145/2525314.2525350 dblp:conf/gis/ArmitiG13 fatcat:ybvf5a5xxzdfbd62b7s6bdtff4

Multiscale Planar Graph Generation [article]

Varsha Chauhan, Alexander Gutfraind, Ilya Safro
2019 arXiv   pre-print
An important characteristic of infrastructure networks such as roads, water distribution and other utility systems is that they can be embedded in a plane, therefore to simulate these system we need realistic  ...  The method preserves the structural properties with minimal bias including the planarity of the network, while introducing realistic variability at multiple scales.  ...  As depicted in Figure 16 when the network is rescaled by introducing new elements at only coarse levels, we find larger communities (e.g., mesh structures in case of our input road network) in the generated  ... 
arXiv:1802.09617v2 fatcat:euyxreozynglflkmi2y64j27fu

Subgraph Similarity Search in Large Graphs [article]

Kanigalpula Samanvi, Naveen Sivadasan
2015 arXiv   pre-print
We study the problem of searching an induced subgraph in a large target graph that is most similar to the given query graph.  ...  These local topological informations are then combined to find a target subgraph having highly similar global topology with the given query graph.  ...  Experiment 4: We use our matching algorithm to identify dense subgraphs in large networks. In particular, we search for dense subgraphs in DBLP and google plus networks.  ... 
arXiv:1512.05256v1 fatcat:zbxyuzqmhbfn7f52gdh44jfdlm

Multiscale planar graph generation

Varsha Chauhan, Alexander Gutfraind, Ilya Safro
2019 Applied Network Science  
An important characteristic of infrastructure networks such as roads, water distribution and other utility systems is that they can be (almost fully) embedded in a plane, therefore to simulate these system  ...  The method preserves the structural properties with minimal bias including the planarity of the network, while introducing realistic variability at multiple scales.  ...  Availability of data and materials All datasets and algorithm implementation presented in this work are available at  ... 
doi:10.1007/s41109-019-0142-3 fatcat:yvc2vao4crhoza3l57j5rmne6i

Road extraction in suburban areas by region-based road subgraph extraction and evaluation

Anne Grote, Christian Heipke, Franz Rottensteiner, Hannes Meyer
2009 2009 Joint Urban Remote Sensing Event  
In order to combine these road parts, neighbouring road parts are connected to a road subgraph if there is evidence that they belong to the same road, such as similar direction and smooth continuation.  ...  This process allows several branches in the subgraph which is why another step follows to evaluate the subgraphs and divide them at gaps which show weak connections.  ...  Today, especially in urban areas roads are to a large degree still extracted manually.  ... 
doi:10.1109/urs.2009.5137676 fatcat:cgeiap6cs5efjbu3awnbza5bgu

Multi-attributed Community Search in Road-social Networks [article]

Fangda Guo, Ye Yuan, Guoren Wang, Xiangguo Zhao, Hao Sun
2021 arXiv   pre-print
Typically, in the face of such a problem setting, we can model the network as a multi-attributed road-social network, in which each user is linked with location information and d (≥ 1) numerical attributes  ...  Given a location-based social network, how to find the communities that are highly relevant to query users and have top overall scores in multiple attributes according to user preferences?  ...  In fact, we find that in real world attributes are usually correlated or more, e.g., most users in Yelp have an attribute value of 0.  ... 
arXiv:2101.09668v2 fatcat:declcqq2mjff7fhaoidmhm6dsy

MRAttractor: Detecting Communities from Large-Scale Graphs [article]

Nguyen Vo, Kyumin Lee, Thanh Tran
2018 arXiv   pre-print
To overcome the drawback and handle large-scale graphs, in this paper we propose MRAttractor, an advanced version of Attractor to be runnable on a MapReduce framework.  ...  Experimental results show that our algorithm significantly reduced running time and was able to handle large-scale graphs.  ...  Texas Road is a road network where nodes are intersections and endpoints. Edges are roads connecting them in Texas. Youtube dataset is a snapshot of friendship graph on Youtube.  ... 
arXiv:1806.08895v1 fatcat:swtqwv2rzzf6zafutscqh3pd7a

Online shortest path computation using Live Traffic index

Dekonda Sindhuja, R Vasavi, A Kousar Nikhath
2015 International Journal of Engineering Trends and Technoloy  
Online Shortest path computation using live traffic index in road networks aims at computing the shortest path from source to destination using Live traffic index.In this paper,index transmission model  ...  cost at client side ,small broadcast size and maintainence time at server side are the extra features achieved in the system.  ...  To summarize, our work makes the following contributions: 1) The traffic provider collects the traffic data of road networks via road sensors and broadcasts to the traffic broadcast server. 2) The traffic  ... 
doi:10.14445/22315381/ijett-v25p227 fatcat:qirrv2u4u5aozfrl44mpg65uye

Learning Graph Topological Features via GAN [article]

Weiyi Liu, Hal Cooper, Min Hwan Oh, Sailung Yeung, Pin-Yu Chen, Toyotaro Suzumura, Lingli Chen
2019 arXiv   pre-print
Inspired by the generation power of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological features from a single  ...  Experiments show that our method produces subgraphs retaining a wide range of topological features, even in early reconstruction stages (unlike a single GAN, which cannot easily identify such features,  ...  Road Network Stages Analysis: We observe in Table 1 that the retained edge percentages of the RoadNet reconstruction decrease more consistently with each stage than in the BA network.  ... 
arXiv:1709.03545v5 fatcat:6jvdodgbrbfknc6ek4t6wjs7bq

On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network

Tobias Skovgaard Jepsen, Christian S. Jensen, Thomas Dyhre Nielsen, Kristian Torp
2018 2018 IEEE International Conference on Big Data (Big Data)  
We analyze the use of network embedding methods, specifically node2vec, for learning road segment embeddings in road networks.  ...  However, these methods have so far mostly been used in the context of social networks, which differ significantly from road networks in terms of, e.g., node degree and level of homophily (which are key  ...  ACKNOWLEDGMENTS This research was supported in part by the DiCyPS project and by grants from the Obel Family Foundation and the Villum Foundation.  ... 
doi:10.1109/bigdata.2018.8622416 dblp:conf/bigdataconf/JepsenJNT18 fatcat:4wgpwxe7r5dzhjx35jy447r2sm
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