Link-Level Network Topology Generation

Akgun, Mehmet Burak; Güneş, Mehmet Hadi

doi:10.1109/icdcsw.2011.25

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…This directly applies to recent work that uses TraceNET [24] -a traceroutelike active probing tool, to discover subnet structures. Subnetlevel topology information has also been considered in several recent studies, e.g., [4,18]. Our approach would also improve the success rate of other active probe-based topology discovery schemes such as Ally [23] -a probe-based IP alias resolver.…”

Section: Related Workmentioning

confidence: 97%

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Location matters: Eliciting responses to direct probes

Blanton

Tozal

Sarac³

et al. 2013

2013 IEEE 32nd International Performance Computing and Communications Conference (IPCCC)

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Abstract-In this work, we propose techniques to attain visibility into an arbitrary Internet subnetwork that is responsive to indirect probes but not to direct probes. By probing the network from a small number of selected vantage points, we are able to collect information about network-layer topology which would otherwise be hidden from measurement due to rate limiting practices, security mechanisms, and routing dynamics. We investigate the reasons for differing visibility, and the required number and placement strategies of vantage points needed to collect topology information at a low cost. We demonstrate substantial improvement in global visibility as probed by the TraceNET path measurement tool when leveraging only five vantage points selected according to route similarity.

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Section: Related Workmentioning

confidence: 97%

“…For this data set, we first performed a TraceNET to each of the 447 unique IP addresses yielded by resolving the Alexa Top 500 web sites 4 from a single location at Purdue University. From this data we extracted 509 hidden subnets.…”

Section: G Probing the Internet At Largementioning

confidence: 99%

Location matters: Eliciting responses to direct probes

Blanton

Tozal

Sarac³

et al. 2013

2013 IEEE 32nd International Performance Computing and Communications Conference (IPCCC)

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“…These topologies misrepresent the Internet as they ignore the multi-access links, which appear very frequently in the Internet backbone as measured in [14]. In modeling network topologies, we should consider the multi-access links [15] in addition to the observed degree distribution of the Internet [16]. In a topological perspective multi-access links have two major implications; pairwise onehop connectivity and a collision domain between subnetwork devices.…”

Section: Analysis Of Internet Topology Modelsmentioning

confidence: 99%

Impact of Multi-access Links on the Internet Topology Modeling

Akgun

Güneş

2013

2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems

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Abstract-Comprehensive analyses that aim to better understand the topology of real world networks have been an important research challenge. Internet topology measurement studies provide samples of the underlying network at various levels. Although router-level Internet topology measurement systems target low level Internet infrastructure, they primarily focus on the Layer-3 connectivity and ignore the underlying multi-access links. In this paper, in addition to the thoroughly studied degree distribution, we analyze the subnet and interface distributions of major Internet topology datasets. We also investigate the impact of the higher granularity modeling at link level versus router level modeling. Our analysis establishes a foundation for the Layer-2 Internet topology generation and introduces the link layer characteristics into the network modeling.

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“…Newman provided the first study on co-authorship networks by analyzing the macroscopic properties of different domains [6,7]. Similarly, researchers have studied academic ties [8], air transport [9], authors network [10], citation networks [11,12], friend recommendation [13], influenza spread [14,15], Internet topology [16][17][18], news networks [19,20], patent networks [21,22], protein interactions [23], software collaborations [24,25], and video industry [26] as complex networks.…”

Section: Introductionmentioning

confidence: 99%

Complex Network Analysis of Research Funding: A Case Study of NSF Grants

Kardes¹,

Sevincer²,

Güneş

et al. 2014

Lecture Notes in Social Networks

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Abstract.Funding from the government agencies has been the driving force for the research and educational institutions particularly in the United States. The government funds billions of dollars every year to lead research initiatives that will shape the future. In this paper, we analyze the funds distributed by the United States National Science Foundation (NSF), a major source of academic research funding, to understand the collaboration patterns among researchers and institutions. Using complex network analysis, we interpret the collaboration patterns at researcher, institution, and state levels by constructing the corresponding networks based on the number of grants collaborated at different time frames. Additionally, we analyze these networks for small, medium, and large projects in order to observe collaboration at different funding levels. We further analyze the directorates to identify the differences in collaboration trends between disciplines. Sample networks can be found at

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Link-Level Network Topology Generation

Cited by 7 publications

References 36 publications

Location matters: Eliciting responses to direct probes

Location matters: Eliciting responses to direct probes

Impact of Multi-access Links on the Internet Topology Modeling

Complex Network Analysis of Research Funding: A Case Study of NSF Grants

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