On the Interplay between Social and Topical Structure

People's interests and people's social relationships are intuitively connected, but understanding their interplay and whether they can help predict each other has remained an open question. We examine the interface of two decisive structures forming the backbone of online social media: the graph structure of social networks - who connects with whom - and the set structure of topical affiliations - who is interested in what. In studying this interface, we identify key relationships whereby each of these structures can be understood in terms of the other. The context for our analysis is Twitter, a complex social network of both follower relationships and communication relationships. On Twitter, "hashtags" are used to label conversation topics, and we examine hashtag usage alongside these social structures. We find that the hashtags that users adopt can predict their social relationships, and also that the social relationships between the initial adopters of a hashtag can predict the future popularity of that hashtag. By studying weighted social relationships, we observe that while strong reciprocated ties are the easiest to predict from hashtag structure, they are also much less useful than weak directed ties for predicting hashtag popularity. Importantly, we show that computationally simple structural determinants can provide remarkable performance in both tasks. While our analyses focus on Twitter, we view our findings as broadly applicable to topical affiliations and social relationships in a host of diverse contexts, including the movies people watch, the brands people like, or the locations people frequent.Comment: 11 page

Provably and Efficiently Approximating Near-cliques using the Tur\'an Shadow: PEANUTS

Clique and near-clique counts are important graph properties with applications in graph generation, graph modeling, graph analytics, community detection among others. They are the archetypal examples of dense subgraphs. While there are several different definitions of near-cliques, most of them share the attribute that they are cliques that are missing a small number of edges. Clique counting is itself considered a challenging problem. Counting near-cliques is significantly harder more so since the search space for near-cliques is orders of magnitude larger than that of cliques. We give a formulation of a near-clique as a clique that is missing a constant number of edges. We exploit the fact that a near-clique contains a smaller clique, and use techniques for clique sampling to count near-cliques. This method allows us to count near-cliques with 1 or 2 missing edges, in graphs with tens of millions of edges. To the best of our knowledge, there was no known efficient method for this problem, and we obtain a 10x - 100x speedup over existing algorithms for counting near-cliques. Our main technique is a space-efficient adaptation of the Tur\'an Shadow sampling approach, recently introduced by Jain and Seshadhri (WWW 2017). This approach constructs a large recursion tree (called the Tur\'an Shadow) that represents cliques in a graph. We design a novel algorithm that builds an estimator for near-cliques, using an online, compact construction of the Tur\'an Shadow.Comment: The Web Conference, 2020 (WWW

Increased cardiac involvement in Fabry disease using blood-corrected native T1 mapping

Fabry disease (FD) is a rare lysosomal storage disorder resulting in myocardial sphingolipid accumulation which is detectable by cardiovascular magnetic resonance as low native T1. However, myocardial T1 contains signal from intramyocardial blood which affects variability and consequently measurement precision and accuracy. Correction of myocardial T1 by blood T1 increases precision. We therefore deployed a multicenter study of FD patients (n = 218) and healthy controls (n = 117) to investigate if blood-correction of myocardial native T1 increases the number of FD patients with low T1, and thus reclassifies FD patients as having cardiac involvement. Cardiac involvement was defined as a native T1 value 2 standard deviations below site-specific means in healthy controls for both corrected and uncorrected measures. Overall low T1 was 135/218 (62%) uncorrected vs. 145/218 (67%) corrected (p = 0.02). With blood-correction, 13/83 previously normal patients were reclassified to low T1. This reclassification appears clinically relevant as 6/13 (46%) of reclassified had focal late gadolinium enhancement or left ventricular hypertrophy as signs of cardiac involvement. Blood-correction of myocardial native T1 increases the proportion of FD subjects with low myocardial T1, with blood-corrected results tracking other markers of cardiac involvement. Blood-correction may potentially offer earlier detection and therapy initiation, but merits further prospective studies

Influence Diffusion in Social Networks under Time Window Constraints

We study a combinatorial model of the spread of influence in networks that generalizes existing schemata recently proposed in the literature. In our model, agents change behaviors/opinions on the basis of information collected from their neighbors in a time interval of bounded size whereas agents are assumed to have unbounded memory in previously studied scenarios. In our mathematical framework, one is given a network $G=(V,E)$, an integer value $t(v)$ for each node $v\in V$, and a time window size $\lambda$. The goal is to determine a small set of nodes (target set) that influences the whole graph. The spread of influence proceeds in rounds as follows: initially all nodes in the target set are influenced; subsequently, in each round, any uninfluenced node $v$ becomes influenced if the number of its neighbors that have been influenced in the previous $\lambda$ rounds is greater than or equal to $t(v)$. We prove that the problem of finding a minimum cardinality target set that influences the whole network $G$ is hard to approximate within a polylogarithmic factor. On the positive side, we design exact polynomial time algorithms for paths, rings, trees, and complete graphs.Comment: An extended abstract of a preliminary version of this paper appeared in: Proceedings of 20th International Colloquium on Structural Information and Communication Complexity (Sirocco 2013), Lectures Notes in Computer Science vol. 8179, T. Moscibroda and A.A. Rescigno (Eds.), pp. 141-152, 201

High-resolution transthoracic echocardiography accurately detects pulmonary arterial pressure and decreased right ventricular contractility in a mouse model of pulmonary fibrosis and secondary pulmonary hypertension

BACKGROUND: To date, assessment of right ventricular (RV) function in mice has relied extensively on invasive measurements. Echocardiographic advances have allowed adaptation of measures used in humans for serial, noninvasive RV functional assessment in mice. We evaluated the diagnostic performance of tricuspid annular plane systolic excursion (TAPSE), RV peak systolic myocardial velocity (s'), RV myocardial performance index (MPI), and RV fractional area change (FAC) in a mouse model of pulmonary hypertension. METHODS AND RESULTS: Echocardiography was performed on mice at baseline and 3 weeks after induction of pulmonary hypertension using inhaled bleomycin or saline, including adapted measures of TAPSE, s', MPI, and FAC. RV systolic pressure was measured by invasive catheterization, and RV contractility was measured as the peak slope of the RV systolic pressure recording (maximum change pressure/change time). Postmortem morphological assessment of RV hypertrophy was performed. RV systolic pressure was elevated and maximum change pressure/change time was reduced in bleomycin versus control (n=8; P=0.002). Compared with controls, bleomycin mice had reduced TAPSE (0.79±0.05 versus 1.06±0.04 mm; P=0.003), s' (21.3±1.2 versus 29.2±1.3 mm/s; P<0.001), and FAC (20.3±0.7% versus 31.0±1.3%; P<0.001), whereas MPI was increased (0.51±0.03 versus 0.37±0.01; P=0.006). All measures correlated with RV systolic pressure and maximum change pressure/change time. Intraobserver and interobserver variability were minimal. Receiver operating characteristic curves demonstrated that TAPSE (<0.84 mm), s'(<23.3 mm/s), MPI (0.42), and FAC (<23.3%) identified maximum change pressure/change time ≤2100 mm Hg/s with high accuracy. CONSLUSIONS: TAPSE, s', MPI, and FAC are measurable consistently using high-resolution echocardiography in mice, and are sensitive and specific measures of pulmonary pressure and RV function. This validation opens the opportunity for serial noninvasive measures in mouse models of pulmonary hypertension, enhancing the statistical power of preclinical studies of novel therapeutics

Design and validation of Segment - freely available software for cardiovascular image analysis

<p>Abstract</p> <p>Background</p> <p>Commercially available software for cardiovascular image analysis often has limited functionality and frequently lacks the careful validation that is required for clinical studies. We have already implemented a cardiovascular image analysis software package and released it as freeware for the research community. However, it was distributed as a stand-alone application and other researchers could not extend it by writing their own custom image analysis algorithms. We believe that the work required to make a clinically applicable prototype can be reduced by making the software extensible, so that researchers can develop their own modules or improvements. Such an initiative might then serve as a bridge between image analysis research and cardiovascular research. The aim of this article is therefore to present the design and validation of a cardiovascular image analysis software package (Segment) and to announce its release in a source code format.</p> <p>Results</p> <p>Segment can be used for image analysis in magnetic resonance imaging (MRI), computed tomography (CT), single photon emission computed tomography (SPECT) and positron emission tomography (PET). Some of its main features include loading of DICOM images from all major scanner vendors, simultaneous display of multiple image stacks and plane intersections, automated segmentation of the left ventricle, quantification of MRI flow, tools for manual and general object segmentation, quantitative regional wall motion analysis, myocardial viability analysis and image fusion tools. Here we present an overview of the validation results and validation procedures for the functionality of the software. We describe a technique to ensure continued accuracy and validity of the software by implementing and using a test script that tests the functionality of the software and validates the output. The software has been made freely available for research purposes in a source code format on the project home page <url>http://segment.heiberg.se</url>.</p> <p>Conclusions</p> <p>Segment is a well-validated comprehensive software package for cardiovascular image analysis. It is freely available for research purposes provided that relevant original research publications related to the software are cited.</p