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Autonomous Overlapping Community Detection in Temporal Networks: A Dynamic Bayesian Nonnegative Matrix Factorization Approach.

A wide variety of natural or artificial systems can be modeled as time-varying or temporal networks. To understand the structural and functional properties of these time-varying networked systems, it is desirable to detect and analyze the evolving community structure. In temporal networks, the identified communities should reflect the current snapshot network, and at the same time be similar to the communities identified in history or say the previous snapshot networks. Most of the existing approaches assume that the number of communities is known or can be obtained by some heuristic methods. This is unsuitable and complicated for most real world networks, especially temporal networks. In this paper, we propose a Bayesian probabilistic model, named Dynamic Bayesian Nonnegative Matrix Factorization (DBNMF), for automatic detection of overlapping communities in temporal networks. Our model can not only give the overlapping community structure based on the probabilistic memberships of nodes in each snapshot network but also automatically determines the number of communities in each snapshot network based on automatic relevance determination. Thereafter, a gradient descent algorithm is proposed to optimize the objective function of our DBNMF model. The experimental results using both synthetic datasets and real-world temporal networks demonstrate that the DBNMF model has superior performance compared with two widely used methods, especially when the number of communities is unknown and when the network is highly sparse

Collective decision making and social interaction rules in mixed-species flocks of songbirds

Associations in mixed-species foraging groups are common in animals, yet have rarely been explored in the context of collective behaviour. Despite many investigations into the social and ecological conditions under which individuals should form groups, we still know little about the specific behavioural rules that individuals adopt in these contexts, or whether these can be generalized to heterospecifics. Here, we studied collective behaviour in flocks in a community of five species of woodland passerine birds. We adopted an automated data collection protocol, involving visits by RFID-tagged birds to feeding stations equipped with antennae, over two winters, recording 91 576 feeding events by 1904 individuals. We demonstrated highly synchronized feeding behaviour within patches, with birds moving towards areas of the patch with the largest proportion of the flock. Using a model of collective decision making, we then explored the underlying decision rule birds may be using when foraging in mixed-species flocks. The model tested whether birds used a different decision rule for conspecifics and heterospecifics, and whether the rules used by individuals of different species varied. We found that species differed in their response to the distribution of conspecifics and heterospecifics across foraging patches. However, simulating decisions using the different rules, which reproduced our data well, suggested that the outcome of using different decision rules by each species resulted in qualitatively similar overall patterns of movement. It is possible that the decision rules each species uses may be adjusted to variation in mean species abundance in order for individuals to maintain the same overall flock-level response. This is likely to be important for maintaining coordinated behaviour across species, and to result in quick and adaptive flock responses to food resources that are patchily distributed in space and time

An Arduino-Based RFID Platform for Animal Research

Radio Frequency Identification (RFID) technology has been broadly applied in the biological sciences to yield new insights into behavior, cognition, population biology, and distributions. RFID systems entail wireless communication between small tags that, when stimulated by an appropriate radio frequency transmission, emit a weak, short-range wireless signal that conveys a unique ID number. These tags, which often operate without a battery, can be attached to animals such that their presence at a particular location can be detected by an RFID reader. This paper describes an RFID data-logging system that can serve as the core for a wide variety of field and laboratory applications for monitoring the activities of individual animals. The core electronics are modeled on an Arduino circuit board, which is a hobbyist electronics system. Users can customize the hardware and software to accommodate their needs. We demonstrate the utility of the system with cursory descriptions of three real-world research applications. The first is a large-scale deployment that was used to examine individual breeding behaviors across four local populations of Wood Ducks. The second application employed an array of RFID-enabled bird feeders that allowed for tests of spatial cognition. Third, we describe a nest-box monitoring system that both records visits from breeding birds and administers experimental treatments, such as increasing temperature or playing audio recordings, in accordance to the presence/absence of individual birds. With these examples we do not attempt to relate details with regard to research findings; rather our intent is to demonstrate some of the possibilities enabled by our low-cost RFID system. Detailed descriptions, design files, and code are made available by means of the Open Science Framework.Open Access fees paid for in whole or in part by the University of Oklahoma LibrariesYe

Modern temporal network theory: A colloquium

The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.Comment: Final accepted versio

Social networks in elasmobranchs and teleost fishes

Over the last decade, there has been an exponential increase in studies using social network analysis to describe the structure of animal societies. In this synthesis, we examine the contribution of social network analysis towards developing our understanding of the social organization of elasmobranchs and teleost fishes. We review and discuss the current state of knowledge of the mechanisms and functions underpinning social network structure in fishes with particular emphasis on cooperation, familiarity, site fidelity, population structure and the welfare of captive populations. We also discuss important methodological issues (e.g. how to identify and mark fish) and highlight new developments in this area of research and their implications for the study of fish behaviour. Finally, we outline promising future research areas for the application of social network analysis to teleost fishes and elasmobranchs