103,955 research outputs found
Scaling of the risk landscape drives optimal life history strategies and the evolution of grazing
Consumers face numerous risks that can be minimized by incorporating
different life-history strategies. How much and when a consumer adds to its
energetic reserves or invests in reproduction are key behavioral and
physiological adaptations that structure much of how organisms interact. Here
we develop a theoretical framework that explicitly accounts for stochastic
fluctuations of an individual consumer's energetic reserves while foraging and
reproducing on a landscape with resources that range from uniformly distributed
to highly clustered. First, we show that optimal life-history strategies vary
in response to changes in the mean productivity of the resource landscape,
where depleted environments promote reproduction at lower energetic states,
greater investment in each reproduction event, and smaller litter sizes. We
then show that if resource variance scales with body size due to landscape
clustering, consumers that forage for clustered foods are susceptible to strong
Allee effects, increasing extinction risk. Finally, we show that the proposed
relationship between consumer body size, resource clustering, and Allee
effect-induced population instability offers key ecological insights into the
evolution of large-bodied grazing herbivores from small-bodied browsing
ancestors.Comment: 9 pages, 5 figures, 3 Supplementary Appendices, 2 Supplementary
Figure
Self-adaptive GA, quantitative semantic similarity measures and ontology-based text clustering
As the common clustering algorithms use vector space model (VSM) to represent document, the conceptual relationships between related terms which do not co-occur literally are ignored. A genetic algorithm-based clustering technique, named GA clustering, in conjunction with ontology is proposed in this article to overcome this problem. In general, the ontology measures can be partitioned into two categories: thesaurus-based methods and corpus-based methods. We take advantage of the hierarchical structure and the broad coverage taxonomy of Wordnet as the thesaurus-based ontology. However, the corpus-based method is rather complicated to handle in practical application. We propose a transformed latent semantic analysis (LSA) model as the corpus-based method in this paper. Moreover, two hybrid strategies, the combinations of the various similarity measures, are implemented in the clustering experiments. The results show that our GA clustering algorithm, in conjunction with the thesaurus-based and the LSA-based method, apparently outperforms that with other similarity measures. Moreover, the superiority of the GA clustering algorithm proposed over the commonly used k-means algorithm and the standard GA is demonstrated by the improvements of the clustering performance
Social dilemmas in an online social network: the structure and evolution of cooperation
We investigate two paradigms for studying the evolution of
cooperation--Prisoner's Dilemma and Snowdrift game in an online friendship
network obtained from a social networking site. We demonstrate that such social
network has small-world property and degree distribution has a power-law tail.
Besides, it has hierarchical organizations and exhibits disassortative mixing
pattern. We study the evolutionary version of the two types of games on it. It
is found that enhancement and sustainment of cooperative behaviors are
attributable to the underlying network topological organization. It is also
shown that cooperators can survive when confronted with the invasion of
defectors throughout the entire ranges of parameters of both games. The
evolution of cooperation on empirical networks is influenced by various network
effects in a combined manner, compared with that on model networks. Our results
can help understand the cooperative behaviors in human groups and society.Comment: 14 pages, 7 figure
Community Detection on Evolving Graphs
Clustering is a fundamental step in many information-retrieval and data-mining applications. Detecting clusters in graphs is also a key tool for finding the community structure in social and behavioral networks. In many of these applications, the input graph evolves over time in a continual and decentralized manner, and, to maintain a good clustering, the clustering algorithm needs to repeatedly probe the graph. Furthermore, there are often limitations on the frequency of such probes, either imposed explicitly by the online platform (e.g., in the case of crawling proprietary social networks like twitter) or implicitly because of resource limitations (e.g., in the case of crawling the web). In this paper, we study a model of clustering on evolving graphs that captures this aspect of the problem. Our model is based on the classical stochastic block model, which has been used to assess rigorously the quality of various static clustering methods. In our model, the algorithm is supposed to reconstruct the planted clustering, given the ability to query for small pieces of local information about the graph, at a limited rate. We design and analyze clustering algorithms that work in this model, and show asymptotically tight upper and lower bounds on their accuracy. Finally, we perform simulations, which demonstrate that our main asymptotic results hold true also in practice
Prisoner's dilemma in structured scale-free networks
The conventional wisdom is that scale-free networks are prone to cooperation
spreading. In this paper we investigate the cooperative behaviors on the
structured scale-free network. On the contrary of the conventional wisdom that
scale-free networks are prone to cooperation spreading, the evolution of
cooperation is inhibited on the structured scale-free network while performing
the prisoner's dilemma (PD) game. Firstly, we demonstrate that neither the
scale-free property nor the high clustering coefficient is responsible for the
inhibition of cooperation spreading on the structured scale-free network. Then
we provide one heuristic method to argue that the lack of age correlations and
its associated `large-world' behavior in the structured scale-free network
inhibit the spread of cooperation. The findings may help enlighten further
studies on evolutionary dynamics of the PD game in scale-free networks.Comment: Definitive version accepted for publication in Journal of Physics
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