Stochastic blockmodels and community structure in networks

Stochastic blockmodels have been proposed as a tool for detecting community structure in networks as well as for generating synthetic networks for use as benchmarks. Most blockmodels, however, ignore variation in vertex degree, making them unsuitable for applications to real-world networks, which typically display broad degree distributions that can significantly distort the results. Here we demonstrate how the generalization of blockmodels to incorporate this missing element leads to an improved objective function for community detection in complex networks. We also propose a heuristic algorithm for community detection using this objective function or its non-degree-corrected counterpart and show that the degree-corrected version dramatically outperforms the uncorrected one in both real-world and synthetic networks.Comment: 11 pages, 3 figure

Scalable Tensor Factorizations for Incomplete Data

The problem of incomplete data - i.e., data with missing or unknown values - in multi-way arrays is ubiquitous in biomedical signal processing, network traffic analysis, bibliometrics, social network analysis, chemometrics, computer vision, communication networks, etc. We consider the problem of how to factorize data sets with missing values with the goal of capturing the underlying latent structure of the data and possibly reconstructing missing values (i.e., tensor completion). We focus on one of the most well-known tensor factorizations that captures multi-linear structure, CANDECOMP/PARAFAC (CP). In the presence of missing data, CP can be formulated as a weighted least squares problem that models only the known entries. We develop an algorithm called CP-WOPT (CP Weighted OPTimization) that uses a first-order optimization approach to solve the weighted least squares problem. Based on extensive numerical experiments, our algorithm is shown to successfully factorize tensors with noise and up to 99% missing data. A unique aspect of our approach is that it scales to sparse large-scale data, e.g., 1000 x 1000 x 1000 with five million known entries (0.5% dense). We further demonstrate the usefulness of CP-WOPT on two real-world applications: a novel EEG (electroencephalogram) application where missing data is frequently encountered due to disconnections of electrodes and the problem of modeling computer network traffic where data may be absent due to the expense of the data collection process

Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition

Tensor decompositions are used in various data mining applications from social network to medical applications and are extremely useful in discovering latent structures or concepts in the data. Many real-world applications are dynamic in nature and so are their data. To deal with this dynamic nature of data, there exist a variety of online tensor decomposition algorithms. A central assumption in all those algorithms is that the number of latent concepts remains fixed throughout the entire stream. However, this need not be the case. Every incoming batch in the stream may have a different number of latent concepts, and the difference in latent concepts from one tensor batch to another can provide insights into how our findings in a particular application behave and deviate over time. In this paper, we define "concept" and "concept drift" in the context of streaming tensor decomposition, as the manifestation of the variability of latent concepts throughout the stream. Furthermore, we introduce SeekAndDestroy, an algorithm that detects concept drift in streaming tensor decomposition and is able to produce results robust to that drift. To the best of our knowledge, this is the first work that investigates concept drift in streaming tensor decomposition. We extensively evaluate SeekAndDestroy on synthetic datasets, which exhibit a wide variety of realistic drift. Our experiments demonstrate the effectiveness of SeekAndDestroy, both in the detection of concept drift and in the alleviation of its effects, producing results with similar quality to decomposing the entire tensor in one shot. Additionally, in real datasets, SeekAndDestroy outperforms other streaming baselines, while discovering novel useful components.Comment: 16 Pages, Accepted at ECML-PKDD 201

Comparative efficacy and safety of bimekizumab in axial spondyloarthritis: a systematic literature review and network meta-analysis

OBJECTIVES: To compare the efficacy and safety of bimekizumab 160 mg every 4 weeks, a selective inhibitor of interleukin‑17F and 17A, with biologic/targeted synthetic disease-modifying anti-rheumatic drugs (b/tsDMARDs) in non-radiographic axial spondyloarthritis (nr-axSpA) and ankylosing spondylitis (AS). METHODS: A systematic literature review identified randomised controlled trials until January 2023 for inclusion in Bayesian network meta-analyses (NMAs), including three b/tsDMARDs exposure networks: predominantly-naïve, naïve, and experienced. Outcomes were Assessment of SpondyloArthritis international Society (ASAS)20, ASAS40, and ASAS partial remission (PR) response rates at 12-16 weeks. A safety NMA investigated discontinuations due to any reason and serious adverse events at 12-16 weeks. RESULTS: The NMA included 36 trials. The predominantly-naïve network provided the most comprehensive results. In the predominantly-naïve nr-axSpA analysis, bimekizumab had significantly higher ASAS20 response rates vs secukinumab 150 mg (with loading dose [LD]/without LD), and comparable response rates vs other active comparators. In the predominantly-naïve AS analysis, bimekizumab had significantly higher ASAS40 response rates vs secukinumab 150 mg (without LD), significantly higher ASAS-PR response rates vs secukinumab 150 mg (with LD), and comparable response rates vs other active comparators. Bimekizumab demonstrated similar safety to other b/tsDMARDs. CONCLUSION: Across ASAS outcomes, bimekizumab was comparable to most b/tsDMARDs, including ixekizumab, TNF inhibitors and upadacitinib, and achieved higher response rates vs secukinumab for some ASAS outcomes in predominantly b/tsDMARD-naïve nr-axSpA and AS patients at 12-16 weeks. In a pooled axSpA network, bimekizumab demonstrated comparable safety vs other b/tsDMARDs