27 papers · ranked by Valyu relevance
Neil R Clark, Ruth Dannenfelser, Christopher M Tan, Michael E Komosinski + 1 more
'Michael E Komosinski' "Avi Ma'ayan"] Background The skeleton of complex systems can be represented as networks where vertices represent entities, and edges represent the relations between these entities. Often it is impossible, or expensive, to determine the network structure by experimental validation of the binary…
Rodrigues, Francisco A.
– Inference and prediction are fundamental to the study of complex systems, where network data are often incomplete, inaccurate or obtained indirectly. In this paper, we review recent advances in network sampling and comparison, as well as in link prediction and network reconstruction from time series. We summarise key…
Léo P.M. Diaz, Michael P.H. Stumpf
Network inference is a notoriously challenging problem. Inferred networks are associated with high uncertainty and likely riddled with false positive and false negative interactions. Especially for biological networks we do not have good ways of judging the performance of inference methods against real networks, and…
Ivan Brugere, Brian Gallagher, Tanya Berger‐Wolf
Networks represent relationships between entities in many complex systems, spanning from online social interactions to biological cell development and brain connectivity. In many cases, relationships between entities are unambiguously known: are two users 'friends' in a social network? Do two researchers collaborate on…
Kestutis Baltakys
Many of the real-world data sets can be portrayed as bipartite networks. Since connections between nodes of the same type are lacking, they need to be inferred. The standard way to do this is by converting the bipartite networks to their monopartite projection. However, this simple approach renders an incomplete…
Dinara Sadykova, Jon M. Yearsley, Andrej Aderhold, Frank Dondelinger + 6 more
Network inference models have been widely applied in ecological, genetic and social studies to infer unknown interactions. However, little is known about how well the models perform and whether they produce reliable results when confronted with networks where weak interactions predominate and for different amounts of…
Sergey Dolgov, Dmitry Savostyanov
We consider a problem of inferring contact network from nodal states observed during an epidemiological process. In a black-box Bayesian optimisation framework this problem reduces to a discrete likelihood optimisation over the set of possible networks. The cardinality of this set grows combinatorially with the number…
Vincenzo Perri, Luka V. Petrović, Ingo Scholtes
Many network analysis and graph learning techniques are based on discrete- or continuous-time models of random walks. To apply these methods, it is necessary to infer transition matrices that formalize the underlying stochastic process in an observed graph. For weighted graphs, where weighted edges capture observations…
Meghamala Sinha, Prasad Tadepalli, Stephen A. Ramsey
In order to increase statistical power for learning a causal network, data are often pooled from multiple observational and interventional experiments. However, if the direct effects of interventions are uncertain, multi-experiment data pooling can result in false causal discoveries. We present a new method, “Learn and…
Hoang M. Tran, Satish T.S. Bukkapatnam
Inferring causal structures of real world complex networks from measured time series signals remains an open issue. The current approaches are inadequate to discern between direct versus indirect influences (i.e., the presence or absence of a directed arc connecting two nodes) in the presence of noise, sparse…
Sergey Dolgov, Dmitry Savostyanov
epidemiological data Authors: ['Sergey Dolgov' 'Dmitry Savostyanov'] We consider a problem of inferring contact network from nodal states observed during an epidemiological process. In a black–box Bayesian optimisation framework this problem reduces to a discrete likelihood optimisation over the set of possible…
Dan Geiger, David Heckerman
We examine two types of similarity networks each based on a distinct notion of relevance. For both types of similarity networks we present an efficient inference algorithm that works under the assumption that every event has a nonzero probability of occurrence. Another inference algorithm is developed for type 1…
Victor Paton, Denes Türei, Olga Ivanova, Sophia Müller-Dott + 5 more
We present NetworkCommons, a platform for integrating prior knowledge, omics data, and network inference methods, facilitating their usage and evaluation. NetworkCommons aims to be an infrastructure for the network biology community that supports the development of better methods and benchmarks, by enhancing…
Shengtong Han, Raymond K. W. Wong, Thomas C. M. Lee, Linghao Shen + 3 more
'Shuo-Yen R. Li' 'Xiaodan Fan' 'Xiaodong Cai'] Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this…
Vincenzo Matta, Ali H. Sayed
This work studies the problem of inferring whether an agent is directly influenced by another agent over an adaptive diffusion network. Agent i influences agent j if they are connected (according to the network topology), and if agent j uses the data from agent i to update its online statistic. The solution of this…
Tiziano Squartini, Giulio Cimini, Andrea Gabrielli, Diego Garlaschelli
'Diego Garlaschelli'] Reconstructing weighted networks from partial information is necessary in many important circumstances, e.g. for a correct estimation of systemic risk. It has been shown that, in order to achieve an accurate reconstruction, it is crucial to reliably replicate the empirical degree sequence, which…
Erdogan Taskesen
Motivation: Real-world data often contain measurements with both continuous and discrete values. Despite the availability of many libraries, data sets with mixed data types require intensive pre-processing steps, and it remains a challenge to describe the relationships between variables. The data understanding phase is…
Xu-Wen Wang, Yize Chen, Yang-Yu Liu
Inferring missing links or predicting future ones based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine^1–3^, e-commerce^4^, social media^5^ and criminal intelligence^6^. Numerous methods have been proposed to solve the link prediction…
Xiangjuan Ren, Muzhi Wang, Tingting Qin, Fang Fang + 2 more
Humans naturally seek knowledge, yet integrating vast, fragmented information remains challenging. Traditionally, knowledge acquisition has relied on random walks within network—an unguided and inefficient process. We introduce compressive learning, a framework that embeds higher-order structural features—specifically…
Benjamin Ries, Richard J Gowers, James RB Eastwood, Irfan Alibay + 4 more
Alchemical free energy campaigns can be planned using graph theory by building up networks that contain nodes representing molecules that are connected by possible transformations as edges. We introduce Konnektor, an open-source Python package, for systematically planning, modifying, and analyzing free energy…
Joshua Levy, Carly Bobak, Brock Christensen, Louis Vaickus + 1 more
Network analysis methods are useful to better understand and contextualize relationships between entities. While statistical and machine learning prediction models generally assume independence between actors, network-based statistical methods for social network data allow for dyadic dependence between actors. While…
Zachary Humphreys, Xenophon Evangelopoulos, Stavros Gerolymatos, Edward O. Pyzer-Knapp + 1 more
Graph neural networks have recently met huge success in various inference tasks including materials property prediction amongst many others. Nevertheless, having an inherently locally-based representation capacity as they do, global representation of materials' structures can only only be achieved by expanding the…
Kevin Mildau, Christoph Büschl, Jürgen Zanghellini, Justin J.J. van der Hooft
Computational metabolomics workflows have revolutionized the untargeted metabolomics field. However, the organization and prioritization of metabolite features remains a laborious process. Organizing metabolomics data is often done through mass fragmentation-based spectral similarity grouping, resulting in feature sets…
Authors not listed
Transition state (TS) geometries of chemical reactions are key to understanding reaction mechanisms and estimating kinetic properties. Inferring these directly from 2D reaction graphs offers chemists a powerful tool for rapid and accessible reaction analysis. Quantum chemical methods for computing TSs are…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
Martin Robinson, Alan Bond, Alexandr Simonov, Jie Zhang + 1 more
Recently, we have introduced the use of techniques drawn from Bayesian statistics to recover kinetic and thermodynamic parameters from voltammetric data, and were able to show that the technique of large amplitude ac voltammetry yielded significantly more accurate parameter values than the equivalent dc approach. In…
Matthew Bailey, Mark Wilson
One of the critical tools of persistent homology is the persistence diagram. We demonstrate the applicability of a persistence diagram showing the existence of topological features (here rings in a 2D network) generated over time instead of space as a tool to analyse trajectories of biological networks. We show how the…