An efficient and principled method for detecting communities in networks

A fundamental problem in the analysis of network data is the detection of network communities, groups of densely interconnected nodes, which may be overlapping or disjoint. Here we describe a method for finding overlapping communities based on a principled statistical approach using generative network models. We show how the method can be implemented using a fast, closed-form expectation-maximization algorithm that allows us to analyze networks of millions of nodes in reasonable running times. We test the method both on real-world networks and on synthetic benchmarks and find that it gives results competitive with previous methods. We also show that the same approach can be used to extract nonoverlapping community divisions via a relaxation method, and demonstrate that the algorithm is competitively fast and accurate for the nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl

From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles

The inference of network topologies from relational data is an important problem in data analysis. Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships based on co-occurrences of words in documents. Solving these problems requires techniques to infer significant links in noisy relational data. In this short paper, we propose a new statistical modeling framework to address this challenge. It builds on generalized hypergeometric ensembles, a class of generative stochastic models that give rise to analytically tractable probability spaces of directed, multi-edge graphs. We show how this framework can be used to assess the significance of links in noisy relational data. We illustrate our method in two data sets capturing spatio-temporal proximity relations between actors in a social system. The results show that our analytical framework provides a new approach to infer significant links from relational data, with interesting perspectives for the mining of data on social systems.Comment: 10 pages, 8 figures, accepted at SocInfo201

Effects of Contact Network Models on Stochastic Epidemic Simulations

The importance of modeling the spread of epidemics through a population has led to the development of mathematical models for infectious disease propagation. A number of empirical studies have collected and analyzed data on contacts between individuals using a variety of sensors. Typically one uses such data to fit a probabilistic model of network contacts over which a disease may propagate. In this paper, we investigate the effects of different contact network models with varying levels of complexity on the outcomes of simulated epidemics using a stochastic Susceptible-Infectious-Recovered (SIR) model. We evaluate these network models on six datasets of contacts between people in a variety of settings. Our results demonstrate that the choice of network model can have a significant effect on how closely the outcomes of an epidemic simulation on a simulated network match the outcomes on the actual network constructed from the sensor data. In particular, preserving degrees of nodes appears to be much more important than preserving cluster structure for accurate epidemic simulations.Comment: To appear at International Conference on Social Informatics (SocInfo) 201

Self-regulated charge transfer and band tilt in nm-scale polar GaN films

To date, the generic polarization of Bernardini, Fiorentini and Vanderbilt (PBFV) has been widely used to address the issue of polarity in III-V nitride semiconductors, but improvements in nitride materials and the performance of optoelectronic devices have been limited. The current first-principles calculation for the electronic structures of nm-scale [0001] GaN films show that the internal electric fields and the band tilt of these films are in opposite direction to those predicted by PBFV. Additionally, it is determined that an intrinsic self-regulated charge transfer across the film limits the electrostatic potential difference across the film, which renders the local conduction band energy minimum (at the Ga-terminated surface) approximately equal to the local valence band energy maximum (at the N-terminated surface). This effect is found to occur in films thicker than ~4nm

Percolation in the classical blockmodel

Classical blockmodel is known as the simplest among models of networks with community structure. The model can be also seen as an extremely simply example of interconnected networks. For this reason, it is surprising that the percolation transition in the classical blockmodel has not been examined so far, although the phenomenon has been studied in a variety of much more complicated models of interconnected and multiplex networks. In this paper we derive the self-consistent equation for the size the global percolation cluster in the classical blockmodel. We also find the condition for percolation threshold which characterizes the emergence of the giant component. We show that the discussed percolation phenomenon may cause unexpected problems in a simple optimization process of the multilevel network construction. Numerical simulations confirm the correctness of our theoretical derivations.Comment: 7 pages, 6 figure

Temporal Evolution of the Solar Wind Bulk Velocity atSolar Minimum by Correlating the STEREO A andBPLASTIC Measurements

The two STEREO spacecraft with nearly identical instrumentation were launched near solar activity minimum and they separate by about 45° per year, providing a unique tool to study the temporal evolution of the solar wind. We analyze the solar wind bulk velocity measured by the two PLASTIC plasma instruments onboard the two STEREO spacecraft. During the first half year of our measurements (March - August 2007) we find the typical alternating slow and fast solar wind stream pattern expected at solar minimum. To evaluate the temporal evolution of the solar wind bulk velocity we exclude the spatial variations and calculate the correlation between the solar wind bulk velocity measured by the two spacecraft. We account for the different spacecraft positions in radial distance and longitude by calculating the corresponding time lag. After adjusting for this time lag we compare the solar wind bulk velocity measurements at the two spacecraft and calculate the correlation between the two time-shifted datasets. We show how this correlation decreases as the time difference between two corresponding measurements increases. As a result, the characteristic temporal changes in the solar wind bulk velocity can be inferred. The obtained correlation is 0.95 for a time lag of 0.5 days and 0.85 for 2 day

The interplay of microscopic and mesoscopic structure in complex networks

Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modeling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient algorithm that allows one to separate the contributions of individual nodes and groups of nodes to the network structure. This leads to improved detection accuracy of latent class structure in real world data sets compared to models that focus on group structure alone. Furthermore, the inclusion of hitherto neglected group specific effects in models used to assess the statistical significance of small subgraph (motif) distributions in networks may be sufficient to explain most of the observed statistics. We show the predictive power of such generative models in forecasting putative gene-disease associations in the Online Mendelian Inheritance in Man (OMIM) database. The approach is suitable for both directed and undirected uni-partite as well as for bipartite networks

Interference with oxidative phosphorylation enhances anoxic expression of rice α-amylase genes through abolishing sugar regulation

Rice has the unique ability to express α-amylase under anoxic conditions, a feature that is critical for successful anaerobic germination and growth. Previously, anaerobic conditions were shown to up-regulate the expression of Amy3 subfamily genes (Amy3B/C, 3D, and 3E) in rice embryos. These genes are known to be feedback regulated by the hydrolytic products of starchy endosperm such as the simple sugar glucose. It was found that oxygen deficiency interferes with the repression of Amy3D gene expression imposed by low concentrations of glucose but not with that imposed by higher amounts. This differential anoxic de-repression depending on sugar concentration suggests the presence of two distinct pathways for sugar regulation of Amy3D gene expression. Anoxic de-repression can be mimicked by treating rice embryos with inhibitors of ATP synthesis during respiration. Other sugar-regulated rice α-amylase genes, Amy3B/C and 3E, behave similarly to Amy3D. Treatment with a respiratory inhibitor or anoxia also relieved the sugar repression of the rice CIPK15 gene, a main upstream positive regulator of SnRK1A that is critical for Amy3D expression in response to sugar starvation. SnRK1A accumulation was previously shown to be required for MYBS1 expression, which transactivates Amy3D by binding to a cis-acting element found in the proximal region of all Amy3 subfamily gene promoters (the TA box). Taken together, these results suggest that prevention of oxidative phosphorylation by oxygen deficiency interferes with the sugar repression of Amy3 subfamily gene expression, leading to their enhanced expression in rice embryos during anaerobic germination