Efficient method for estimating the number of communities in a network

While there exist a wide range of effective methods for community detection in networks, most of them require one to know in advance how many communities one is looking for. Here we present a method for estimating the number of communities in a network using a combination of Bayesian inference with a novel prior and an efficient Monte Carlo sampling scheme. We test the method extensively on both real and computer-generated networks, showing that it performs accurately and consistently, even in cases where groups are widely varying in size or structure.Comment: 13 pages, 4 figure

Network reachability of real-world contact sequences

We use real-world contact sequences, time-ordered lists of contacts from one person to another, to study how fast information or disease can spread across network of contacts. Specifically we measure the reachability time -- the average shortest time for a series of contacts to spread information between a reachable pair of vertices (a pair where a chain of contacts exists leading from one person to the other) -- and the reachability ratio -- the fraction of reachable vertex pairs. These measures are studied using conditional uniform graph tests. We conclude, among other things, that the network reachability depends much on a core where the path lengths are short and communication frequent, that clustering of the contacts of an edge in time tend to decrease the reachability, and that the order of the contacts really do make sense for dynamical spreading processes.Comment: (v2: fig. 1 fixed

Epigenetics in Canine Mammary Tumors: Upregulation of miR-18a and miR-18b Oncogenes Is Associated with Decreased ERS1 Target mRNA Expression and ERα Immunoexpression in Highly Proliferating Carcinomas

The expression of miRNAs is one of the main epigenetic mechanisms responsible for the regulation of gene expression in mammals, and in cancer, miRNAs participate by regulating the expression of protein-coding cancer-associated genes. In canine mammary tumors (CMTs), the ESR1 gene encodes for ERa, and represents a major target gene for miR-18a and miR-18b, previously found to be overexpressed in mammary carcinomas. A loss in ERa expression in CMTs is commonly associated with poor prognosis, and it is noteworthy that the downregulation of the ESR1 would appear to be more epigenetic than genetic in nature. In this study, the expression of ESR1 mRNA in formalin-fixed, paraffin-embedded (FFPE) canine mammary tumors (CMTs) was evaluated and compared with the expression levels of miR18a and miR18b, both assessed via RT-qPCR. Furthermore, the possible correlation between the miRNA expression data and the immunohistochemical prognostic factors (ERa immunoexpression; Ki67 proliferative index) was explored. A total of twenty-six FFPE mammary samples were used, including 22 CMTs (7 benign; 15 malignant) and four control samples (three normal mammary glands and one case of lobular hyperplasia). The obtained results demonstrate that miR-18a and miR-18b are upregulated in malignant CMTs, negatively correlating with the expression of target ESR1 mRNA. Of note, the upregulation of miRNAs strictly reflects the progressive loss of ERa immunoexpression and increased tumor cell proliferation as measured using the Ki67 index. The results suggest a central role of miR-18a and miR-18b in the pathophysiology of canine mammary tumors as potential epigenetic mechanisms involved in ERa downregulation. Moreover, as miRNA expression reflects ERa protein status and a high proliferative index, miR-18a and miR-18b may represent promising biomarkers with prognostic value. More detailed investigations on a larger number of cases are needed to better understand the influence of these miRNAs in canine mammary tumors

Evolutionary game dynamics in phenotype space

Evolutionary dynamics can be studied in well-mixed or structured populations. Population structure typically arises from the heterogeneous distribution of individuals in physical space or on social networks. Here we introduce a new type of space to evolutionary game dynamics: phenotype space. The population is well-mixed in the sense that everyone is equally likely to interact with everyone else, but the behavioral strategies depend on distance in phenotype space. Individuals might behave differently towards those who look similar or dissimilar. Individuals mutate to nearby phenotypes. We study the `phenotypic space walk' of populations. We present analytic calculations that bring together ideas from coalescence theory and evolutionary game dynamics. As a particular example, we investigate the evolution of cooperation in phenotype space. We obtain a precise condition for natural selection to favor cooperators over defectors: for a one-dimensional phenotype space and large population size the critical benefit-to-cost ratio is given by b/c=1+2/sqrt{3}. We derive the fundamental condition for any evolutionary game and explore higher dimensional phenotype spaces.Comment: version 2: minor changes; equivalent to final published versio

A tale of two classifier systems

This paper describes two classifier systems that learn. These are rule-based systems that use genetic algorithms, which are based on an analogy with natural selection and genetics, as their principal learning mechanism, and an economic model as their principal mechanism for apportioning credit. CFS-C is a domain-independent learning system that has been widely tested on serial computers. * CFS is a parallel implementation of CFS-C that makes full use of the inherent parallelism of classifier systems and genetic algorithms, and that allows the exploration of large-scale tasks that were formerly impractical. As with other approaches to learning, classifier systems in their current form work well for moderately-sized tasks but break down for larger tasks. In order to shed light on this issue, we present several empirical studies of known issues in classifier systems, including the effects of population size, the actual contribution of genetic algorithms, the use of rule chaining in solving higher-order tasks, and issues of task representation and dynamic population convergence. We conclude with a discussion of some major unresolved issues in learning classifier systems and some possible approaches to making them more effective on complex tasks.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46937/1/10994_2004_Article_BF00113895.pd

Lightweight Interactions for Reciprocal Cooperation in a Social Network Game

The construction of reciprocal relationships requires cooperative interactions during the initial meetings. However, cooperative behavior with strangers is risky because the strangers may be exploiters. In this study, we show that people increase the likelihood of cooperativeness of strangers by using lightweight non-risky interactions in risky situations based on the analysis of a social network game (SNG). They can construct reciprocal relationships in this manner. The interactions involve low-cost signaling because they are not generated at any cost to the senders and recipients. Theoretical studies show that low-cost signals are not guaranteed to be reliable because the low-cost signals from senders can lie at any time. However, people used low-cost signals to construct reciprocal relationships in an SNG, which suggests the existence of mechanisms for generating reliable, low-cost signals in human evolution.Comment: 13 pages, 2 figure

Statistical Mechanics of Dilute Batch Minority Games with Random External Information

We study the dynamics and statics of a dilute batch minority game with random external information. We focus on the case in which the number of connections per agent is infinite in the thermodynamic limit. The dynamical scenario of ergodicity breaking in this model is different from the phase transition in the standard minority game and is characterised by the onset of long-term memory at finite integrated response. We demonstrate that finite memory appears at the AT-line obtained from the corresponding replica calculation, and compare the behaviour of the dilute model with the minority game with market impact correction, which is known to exhibit similar features.Comment: 22 pages, 6 figures, text modified, references updated and added, figure added, typos correcte