Compressing networks with super nodes

Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns. To facilitate community detection in large networks, we recast the network to be partitioned into a smaller network of 'super nodes', each super node comprising one or more nodes in the original network. To define the seeds of our super nodes, we apply the 'CoreHD' ranking from dismantling and decycling. We test our approach through the analysis of two common methods for community detection: modularity maximization with the Louvain algorithm and maximum likelihood optimization for fitting a stochastic block model. Our results highlight that applying community detection to the compressed network of super nodes is significantly faster while successfully producing partitions that are more aligned with the local network connectivity, more stable across multiple (stochastic) runs within and between community detection algorithms, and overlap well with the results obtained using the full network

Enhanced detectability of community structure in multilayer networks through layer aggregation

Many systems are naturally represented by a multilayer network in which edges exist in multiple layers that encode different, but potentially related, types of interactions, and it is important to understand limitations on the detectability of community structure in these networks. Using random matrix theory, we analyze detectability limitations for multilayer (specifically, multiplex) stochastic block models (SBMs) in which L layers are derived from a common SBM. We study the effect of layer aggregation on detectability for several aggregation methods, including summation of the layers' adjacency matrices for which we show the detectability limit vanishes as O(L^{-1/2}) with increasing number of layers, L. Importantly, we find a similar scaling behavior when the summation is thresholded at an optimal value, providing insight into the common - but not well understood - practice of thresholding pairwise-interaction data to obtain sparse network representations.Comment: 7 pages, 4 figure

Stroke Severity Affects Timing: Time From Stroke Code Activation to Initial Imaging is Longer in Patients With Milder Strokes.

Optimizing the time it takes to get a potential stroke patient to imaging is essential in a rapid stroke response. At our hospital, door-to-imaging time is comprised of 2 time periods: the time before a stroke is recognized, followed by the period after the stroke code is called during which the stroke team assesses and brings the patient to the computed tomography scanner. To control for delays due to triage, we isolated the time period after a potential stroke has been recognized, as few studies have examined the biases of stroke code responders. This code-to-imaging time (CIT) encompassed the time from stroke code activation to initial imaging, and we hypothesized that perception of stroke severity would affect how quickly stroke code responders act. In consecutively admitted ischemic stroke patients at The Mount Sinai Hospital emergency department, we tested associations between National Institutes of Health Stroke Scale scores (NIHSS), continuously and at different cutoffs, and CIT using spline regression, t tests for univariate analysis, and multivariable linear regression adjusting for age, sex, and race/ethnicity. In our study population, mean CIT was 26 minutes, and mean presentation NIHSS was 8. In univariate and multivariate analyses comparing CIT between mild and severe strokes, stroke scale scores4

Timing of vessel imaging for suspected large vessel occlusions does not affect groin puncture time in transfer patients with stroke.

BACKGROUND: Access to endovascular therapy (ET) in cases of acute ischemic stroke may be limited, and rapid transfer of eligible patients to hospitals with endovascular capability is needed. OBJECTIVE: To determine the optimal timing of diagnostic CT angiography to confirm large vessel occlusion (LVO). METHODS: Of 57 emergency department transfers to Mount Sinai Hospital (MSH) for possible ET from January 2015 through March 2016, 39 (68%) underwent ET, among whom 22 (56%) had CT angiography before transfer and 17 (44%) had CT angiography on arrival. We compared mean outside hospital arrival to groin puncture (OTG) time between the two groups using t-tests and Wilcoxon rank sum tests. OTG was defined as the difference between groin puncture and outside hospital arrival time minus ambulance travel time. RESULTS: Average age was 73±13 years and average National Institute of Health Stroke Scale score was 19±5. There was no difference in average OTG time between the two groups (191 min for CT angiography at outside hospital vs 190 min for CT angiography at MSH (p=0.99 for t-test and 0.69 for rank sum test)). Among the 18 patients who were transferred but did not receive ET, 10 had no LVO, 5 had large established infarcts on arrival and 3 had post-tissue plasminogen activator hemorrhage. In 9/10 patients without LVO, CT angiography was not performed before transfer. CONCLUSIONS: CT angiography timing in the transfer process does not affect OTG time, but 90% of patients without LVO had not had CT angiography before transfer. Hence, it might be beneficial to obtain a CT angiogram at the outside hospital, if it can be acquired and read rapidly, to avoid the cost and potential clinical deterioration associated with unnecessary transfers

Adapting Community Detection Approaches to Large, Multilayer, and Attributed Networks

Networks have become a common data mining tool to encode relational definitions between a set of entities. Whether studying biological correlations, or communication between individuals in a social network, network analysis tools enable interpretation, prediction, and visualization of patterns in the data. Community detection is a well-developed subfield of network analysis, where the objective is to cluster nodes into 'communities' based on their connectivity patterns. There are many useful and robust approaches for identifying communities in a single, moderately-sized network, but the ability to work with more complicated types of networks containing extra or a large amount of information poses challenges. In this thesis, we address three types of challenging network data and how to adapt standard community detection approaches to handle these situations. In particular, we focus on networks that are large, attributed, and multilayer. First, we present a method for identifying communities in multilayer networks, where there exist multiple relational definitions between a set of nodes. Next, we provide a pre-processing technique for reducing the size of large networks, where standard community detection approaches might have inconsistent results or be prohibitively slow. We then introduce an extension to a probabilistic model for community structure to take into account node attribute information and develop a test to quantify the extent to which connectivity and attribute information align. Finally, we demonstrate example applications of these methods in biological and social networks. This work helps to advance the understand of network clustering, network compression, and the joint modeling of node attributes and network connectivity.Doctor of Philosoph

Enhanced Detectability of Community Structure in Multilayer Networks through Layer Aggregation

Many systems are naturally represented by a multilayer network in which edges exist in multiple layers that encode different, but potentially related, types of interactions, and it is important to understand limitations on the detectability of community structure in these networks. Using random matrix theory, we analyze detectability limitations for multilayer (specifically, multiplex) stochastic block models (SBMs) in which L layers are derived from a common SBM. We study the effect of layer aggregation on detectability for several aggregation methods, including summation of the layers’ adjacency matrices for which we show the detectability limit vanishes as (L−1/2) with increasing number of layers, L. Importantly, we find a similar scaling behavior when the summation is thresholded at an optimal value, providing insight into the common—but not well understood—practice of thresholding pairwise-interaction data to obtain sparse network representations