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12
Comparing predictive power in climate data: Clustering matters
- in Advances in Spatial and Temporal Databases
"... Abstract. Various clustering methods have been applied to climate, ecological, and other environmental datasets, for example to define climate zones, automate land-use classification, and similar tasks. Measuring the “goodness ” of such clusters is generally application-dependent and highly subjecti ..."
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Abstract. Various clustering methods have been applied to climate, ecological, and other environmental datasets, for example to define climate zones, automate land-use classification, and similar tasks. Measuring the “goodness ” of such clusters is generally application-dependent and highly subjective, often requiring domain expertise and/or validation with field data (which can be costly or even impossible to acquire). Here we focus on one particular task: the extraction of ocean climate indices from observed climatological data. In this case, it is possible to quantify the relative performance of different methods. Specifically, we propose to extract indices with complex networks constructed from climate data, which have been shown to effectively capture the dynamical behavior of the global climate system, and compare their predictive power to candidate indices obtained using other popular clustering methods. Our results demonstrate that network-based clusters are statistically significantly better predictors of land climate than any other clustering method, which could lead to a deeper understanding of climate processes and complement physics-based climate models. 1
Complex Networks in Climate Science: Progress, Opportunities and Challenges
- In Proceedings of the 2010 Conference on Intelligent Data Understanding, CIDU 2010, Mountain View
, 2013
"... Abstract. Networks have been used to describe and model a wide range of complex systems, both natural as well as man-made. One particularly interesting application in the earth sciences is the use of complex networks to represent and study the global climate system. In this paper, we motivate this g ..."
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Abstract. Networks have been used to describe and model a wide range of complex systems, both natural as well as man-made. One particularly interesting application in the earth sciences is the use of complex networks to represent and study the global climate system. In this paper, we motivate this general approach, explain the basic methodology, report on the state of the art (including our contributions), and outline open questions and opportunities for future research. 1.
Community detection in large-scale networks: a survey and empirical evaluation. WIREs Comput Stat
, 2014
"... Community detection is a common problem in graph data analytics that consists of finding groups of densely connected nodes with few connections to nodes outside of the group. In particular, identifying communities in large-scale networks is an important task in many scientific domains. In this revi ..."
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Community detection is a common problem in graph data analytics that consists of finding groups of densely connected nodes with few connections to nodes outside of the group. In particular, identifying communities in large-scale networks is an important task in many scientific domains. In this review, we evaluated eight state-of-the-art and five traditional algorithms for overlapping and disjoint community detection on large-scale real-world networks with known ground-truth communities. These 13 algorithms were empirically compared using goodness metrics that measure the structural properties of the identified communities, as well as performance metrics that evaluate these communities against the ground-truth. Our results show that these two types of metrics are not equivalent. That is, an algorithm may perform well in terms of goodness metrics, but poorly in terms of performance metrics, or vice versa.
Anomaly detection in dynamic networks: a survey
- Wiley Interdisciplinary Reviews: Computational Statistics
, 2015
"... Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressivene ..."
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Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressiveness and their natural ability to represent complex relationships. Originally, techniques focused on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data. As real-world networks are constantly changing, there has been a shift in focus to dynamic graphs, which evolve over time. In this survey, we aim to provide a comprehensive overview of anomaly detection in dynamic networks, concentrating on the state-of-the-art methods. We first describe four types of anomalies that arise in dynamic networks, providing an intuitive explanation, applications, and a concrete example for each. Having established an idea for what constitutes an anomaly, a general two-stage approach to anomaly detection in dynamic networks that is common among the methods is presented. We then construct a two-tiered taxonomy, first partitioning the methods based on the intuition behind their approach, and subsequently subdividing them based on the types of anomalies they detect. Within each of the tier one categories-community, compression, decomposition, distance, and probabilistic model based-we highlight the major similarities and differences, showing the wealth of techniques derived from similar conceptual approaches. © 2015 The Authors. financial systems connecting banks across the world, electric power grids connecting geographically distributed areas, and social networks that connect users, businesses, or customers using relationships such as friendship, collaboration, or transactional interactions. These are examples of dynamic networks, which, unlike static networks, are constantly undergoing changes to their structure or attributes. Possible changes include insertion and deletion of vertices (objects), insertion and deletion of edges (relationships), and modification of attributes (e.g., vertex or edge labels). WIREs Computational Statistics An important problem over dynamic networks is anomaly detection-finding objects, relationships, or
Spatially Penalized Regression for Extremes Dependence Analysis and Prediction: Case of Precipitation Extremes
"... The inability to predict precipitation extremes under nonstationary climate remains a crucial science gap. Precipitation is not a state-variable within climate models, exhibits space-time heterogeneities, and is subject to thresholds and intermittences. Atmospheric variables in the spatiotemporal ne ..."
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The inability to predict precipitation extremes under nonstationary climate remains a crucial science gap. Precipitation is not a state-variable within climate models, exhibits space-time heterogeneities, and is subject to thresholds and intermittences. Atmospheric variables in the spatiotemporal neighborhood, like temperature, humidity and updraft velocity, are often better predicted than precipitation from these models, and may have information relevant for precipitation extremes. Model-simulated atmospheric variables have been used to enhance model-predicted precipitation extremes in two ways: statistical downscaling routinely uses regression methods including neural networks and recently physics-based formulations have been developed. The former may not generalize under non-stationary climate while the latter is more interpretable but may not be able to discover or
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"... Towards understanding dominant processes in complex dynamical systems: Case of precipitation extremes ..."
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Towards understanding dominant processes in complex dynamical systems: Case of precipitation extremes
Descriptive Analysis of the Global Climate System and Predictive Modeling for Uncertainty Reduction in Climate Projections using Complex Networks
"... As evidence in support of anthropogenic climate change continues to mount [1], the study of climate has become a focus of scientific research, political attention, and socioeconomic concern. There are many different aspects to studying climate, from the collection and analysis of historical/observed ..."
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As evidence in support of anthropogenic climate change continues to mount [1], the study of climate has become a focus of scientific research, political attention, and socioeconomic concern. There are many different aspects to studying climate, from the collection and analysis of historical/observed data to the understanding of current climate and its underlying physical processes and the development of models for
Empirical Comparison of Correlation Measures and Pruning Levels in Complex Networks Representing the Global Climate System
"... Abstract—Climate change is an issue of growing economic, social, and political concern. Continued rise in the average temperatures of the Earth could lead to drastic climate change or an increased frequency of extreme events, which would negatively affect agriculture, population, and global health. ..."
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Abstract—Climate change is an issue of growing economic, social, and political concern. Continued rise in the average temperatures of the Earth could lead to drastic climate change or an increased frequency of extreme events, which would negatively affect agriculture, population, and global health. One way of studying the dynamics of the Earth’s changing climate is by attempting to identify regions that exhibit similar climatic behavior in terms of long-term variability. Climate networks have emerged as a strong analytics framework for both descriptive analysis and predictive modeling of the emergent phenomena. Previously, the networks were constructed using only one measure of similarity, namely the (linear) Pearson cross correlation, and were then clustered using a community detection algorithm. However, nonlinear dependencies are known to exist in climate, which begs the question whether more complex correlation measures are able to capture any such relationships. In this paper, we present a systematic study of different univariate measures of similarity and compare how each affects both the network structure as well as the predictive power of the clusters. I.
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"... Towards understanding dominant processes in complex dynamical systems: Case of precipitation extremes ..."
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Towards understanding dominant processes in complex dynamical systems: Case of precipitation extremes
Learning Hierarchical Multi-label Classification Trees from Network Data
"... Abstract. We present an algorithm for hierarchical multi-label classifi-cation (HMC) in a network context. It is able to classify instances that may belong to multiple classes at the same time and consider the hierar-chical organization of the classes. It assumes that the instances are placed in a n ..."
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Abstract. We present an algorithm for hierarchical multi-label classifi-cation (HMC) in a network context. It is able to classify instances that may belong to multiple classes at the same time and consider the hierar-chical organization of the classes. It assumes that the instances are placed in a network and uses information on the network connections during the learning of the predictive model. Many real world prediction problems have classes that are organized hierarchically and instances that can have pairwise connections. One example is web document classification, where topics (classes) are typically organized into a hierarchy and documents are connected by hyperlinks. Another example, which is considered in this paper, is gene/protein function prediction, where genes/proteins are connected and form protein-to-protein interaction (PPI) networks. Net-work datasets are characterized by a form of autocorrelation, where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. Combining the hierarchical multi-label classification task with network prediction is thus not trivial and re-quires the introduction of the new concept of network autocorrelation for HMC. The proposed algorithm is able to profitably exploit network autocorrelation when learning a tree-based prediction model for HMC. The learned model is in the form of a Predictive Clustering Tree (PCT) and predicts multiple (hierarchically organized) labels at the leaves. Ex-periments show the effectiveness of the proposed approach for different problems of gene function prediction, considering different PPI networks. The results show that different networks introduce different benefits in different problems of gene function prediction. 1
