arXiv subjects

Constantine Dovrolis

Publications and source records attributed to Constantine Dovrolis.

23 records · Page 2Linked to original sources

arXiv2016

A symmetry-based method to infer structural brain networks from tractography data

Recent progress in diffusion MRI and tractography algorithms as well as the launch of the Human Connectome Project (HCP) have provided brain research with an abundance of structural connectivity data. In this work, we describe and evaluate a method that can infer the structural brain network that interconnects a given set of Regions of Interest (ROIs) from tractography data. The proposed method, referred to as Minimum Asymmetry Network Inference Algorithm (MANIA), differs from prior work because it does not determine the connectivity between two ROIs based on an arbitrary connectivity threshold. Instead, we exploit a basic limitation of the tractography process: the observed streamlines from a source to a target do not provide any information about the polarity of the underlying white matter, and so if there are some fibers connecting two voxels (or two ROIs) X and Y tractography should be able in principle to follow this connection in both directions, from X to Y and from Y to X. We leverage this limitation to formulate the network inference process as an optimization problem that minimizes the (appropriately normalized) asymmetry of the observed network. We evaluate the proposed method on a noise model that randomly corrupts the observed connectivity of synthetic networks. As a case-study, we apply MANIA on diffusion MRI data from 28 healthy subjects to infer the structural network between 18 corticolimbic ROIs that are associated with various neuropsychiatric conditions including depression, anxiety and addiction.

q-bio.NC↗

arXiv2014

Co-evolutionary dynamics in social networks: A case study of Twitter

Complex networks often exhibit co-evolutionary dynamics, meaning that the network topology and the state of nodes or links are coupled, affecting each other in overlapping time scales. We focus on the co-evolutionary dynamics of online social networks, and on Twitter in particular. Monitoring the activity of thousands of Twitter users in real-time, and tracking their followers and tweets/retweets, we propose a method to infer new retweet-driven follower relations. The formation of such relations is much more likely than the exogenous creation of new followers in the absence of any retweets. We identify the most significant factors (reciprocity and the number of retweets that a potential new follower receives) and propose a simple probabilistic model of this effect. We also discuss the implications of such co-evolutionary dynamics on the topology and function of a social network.

cs.SI↗

arXiv2012

DNS-based Ingress Load Balancing: An Experimental Evaluation

Multihomed services can load-balance their incoming connection requests using DNS, resolving the name of the server with different addresses depending on the link load that corresponds to each address. Previous work has studied a number of problems with this approach, e.g., due to Time-to-Live duration violations and client proximity to local DNS servers. In this paper, we experimentally evaluate a DNS-based ingress traffic engineering system that we deployed at Georgia Tech. Our objective is to understand whether simple and robust load balancing algorithms can be accurate in practice, despite aforementioned problems with DNS-based load balancing methods. In particular, we examine the impact of various system parameters and of the main workload characteristics. We show that a window-based measurement scheme can be fairly accurate in practice, as long as its window duration has been appropriately configured.

cs.NI↗

arXiv2011

Incremental Versus Optimized Network Design

Even though the problem of network topology design is often studied as a "clean-slate" optimization, in practice most service-provider and enterprise networks are designed incrementally over time. This evolutionary process is driven by changes in the underlying parameters and constraints (the "environment") and it aims to minimize the modification cost after each change in the environment. In this paper, we first formulate the incremental design approach (in three variations), and compare that with the more traditional optimized design approach in which the objective is to minimize the total network cost. We evaluate the cost overhead and evolvability of incremental design under two network expansion models (random and gradual), comparing incremental and optimized networks in terms of cost, topological similarity, delay and robustness. We find that even though incremental design has some cost overhead, that overhead does not increase as the network grows. Also, it is less costly to evolve an existing network than to design it "from scratch" as long as the network expansion factor is less than a critical value.

cs.NI↗

arXiv2011

Can User-Level Probing Detect and Diagnose Common Home-WLAN Pathologies?

Common WLAN pathologies include low signal-to-noise ratio, congestion, hidden terminals or interference from non-802.11 devices and phenomena. Prior work has focused on the detection and diagnosis of such problems using layer-2 information from 802.11 devices and special-purpose access points and monitors, which may not be generally available. Here, we investigate a userlevel approach: is it possible to detect and diagnose 802.11 pathologies with strictly user-level active probing, without any cooperation from, and without any visibility in, layer-2 devices? In this paper, we present preliminary but promising results indicating that such diagnostics are feasible.

cs.NI↗