{"id":8418,"date":"2016-12-08T01:00:00","date_gmt":"2016-12-07T17:00:00","guid":{"rendered":"http:\/\/swarma.org\/%swarma-paper%\/"},"modified":"2018-12-06T00:47:10","modified_gmt":"2018-12-05T16:47:10","slug":"%e4%b8%93%e9%a2%98%ef%bc%9a%e5%90%ab%e6%97%b6%e7%bd%91%e7%bb%9c%e7%90%86%e8%ae%ba%e5%8f%8a%e5%ba%94%e7%94%a8-%e5%a4%8d%e6%9d%82%e6%96%87%e6%91%98%e7%bf%bb%e8%af%91","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=8418","title":{"rendered":"\u4e13\u9898\uff1a\u542b\u65f6\u7f51\u7edc\u7406\u8bba\u53ca\u5e94\u7528 | \u590d\u6742\u6587\u6458\u7ffb\u8bd1"},"content":{"rendered":"
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\u4ec0\u4e48\u662f\u6807\u5ea6\u975e\u7ebf\u6027\uff1f<\/span><\/strong><\/span><\/p>\n

\u5982\u4f55\u63a7\u5236\u590d\u6742\u7cfb\u7edf\uff1f<\/span><\/strong><\/span><\/p>\n

\u96c6\u667a\u590d\u6742\u6587\u6458\u7ffb\u8bd1\u5c0f\u7ec4\u4e3a\u4f60\u5448\u73b0\u65b0\u9c9c\u7684\u7814\u7a76\u5185\u5bb9\uff01<\/strong><\/p>\n<\/section>\n


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\u4e13\u9898\uff1a\u542b\u65f6\u7f51\u7edc\u7406\u8bba\u53ca\u5e94\u7528 <\/span><\/strong><\/span><\/p>\n

\uff08Modern temporal network theory: a colloquium\uff09<\/span><\/strong><\/span><\/p>\n


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Eur. Phys. J. B (2015) 88: 234 April 16, 7:24 PM<\/span>April 15, 10:34 PM<\/span><\/p>\n<\/blockquote>\n

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BY Petter Holme<\/span><\/p>\n<\/blockquote>\n

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(Translated by -)<\/span><\/strong><\/p>\n<\/blockquote>\n

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\"\u96c6\u667a\"
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 is 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.<\/span><\/p>\n<\/section>\n

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\u539f\u6587\u94fe\u63a5\uff1a<\/span>http:\/\/link.springer.com\/article\/10.1140%2Fepjb%2Fe2015-60657-4<\/a><\/span><\/p>\n<\/section>\n<\/blockquote>\n<\/section>\n<\/section>\n


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\u6807\u5ea6\u975e\u7ebf\u6027\uff1f<\/span><\/strong><\/span><\/p>\n

\uff08Is this scaling nonlinear? \uff09<\/span><\/strong><\/span><\/p>\n


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<\/span>arXiv:1604.02872 physics.soc-ph April 16, 9:20 PM<\/span><\/p>\n<\/blockquote>\n

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BY J. C. Leitao, J.M. Miotto, M. Gerlach, E. G. Altmann<\/span><\/p>\n<\/blockquote>\n

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(Translated by – \u5218\u6e05\u6674)<\/span><\/strong><\/p>\n<\/blockquote>\n

\u8fd110\u5e74\u6765\u590d\u6742\u7cfb\u7edf\u7814\u7a76\u9886\u57df\u4e2d\u6700\u8457\u540d\u7684\u53d1\u73b0\u4e4b\u4e00\u662f\uff1a\u4e0d\u540c\u7684\u6307\u6807y\uff08\u5982\u4e13\u5229\u6570\uff09\u968f\u7740\u57ce\u5e02\u4eba\u53e3x\u975e\u7ebf\u6027\u53d8\u5316\uff0c\u5448\u73b0\u51fa\u67d0\u4e9b\u89c4\u5f8b\uff0c\u5982 y\uff5ex^\u03b2, \u03b2\u22601\u3002\u6700\u8fd1\uff0c\u5728\u4e00\u4e9b\u4f7f\u7528\u4e86\u65b0\u7684\u6570\u636e\u5e93\u5e76\u7528\u4e0d\u540c\u7684\u65b9\u6cd5\u5b9a\u4e49\u57ce\u5e02\u8fb9\u754c\u7684\u7814\u7a76\u4e2d\uff0c\u8fd9\u4e2a\u53d1\u73b0\u7684\u666e\u9002\u6027\u53d7\u5230\u4e86\u8d28\u7591\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u7528\u4e00\u79cd\u6982\u7387\u8bba\u7684\u6846\u67b6\u7814\u7a76\u4e86\u975e\u7ebf\u6027\u6807\u5ea6\u7684\u5b58\u5728\u6027\uff0c\u5728\u7814\u7a76\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u6da8\u843d\u660e\u786e\u5730\u63cf\u8ff0\u51fa\u6765\u3002\u7279\u522b\u5730\uff0c\u6211\u4eec\u53d1\u73b0\u8fd9\u79cd\u65b9\u6cd5\u4e0d\u4ec5\u5141\u8bb8\uff08a\uff09\u4f30\u8ba1\u03b2\u548c\u7f6e\u4fe1\u533a\u95f4\uff0c\u8fd8\u5141\u8bb8\uff08b\uff09\u91cf\u5316\u652f\u6301\u03b2\u22601\u7684\u8bc1\u636e\uff0c\u5e76\u4e14\uff08c\uff09\u68c0\u9a8c\u89c2\u5bdf\u6570\u636e\u7b26\u5408\u975e\u7ebf\u6027\u6807\u5ea6\u7684\u5047\u8bbe\u3002\u6211\u4eec\u4f7f\u7528\u8fd9\u79cd\u7814\u7a76\u601d\u8def\u6765\u6bd4\u8f83\u4e86\u4e3a15\u4e2a\u4e0d\u540c\u7684\u6570\u636e\u5e93\u5efa\u6a21\u76845\u4e2a\u4e0d\u540c\u7684\u6a21\u578b\uff0c\u6211\u4eec\u53d1\u73b0\uff0c\u6570\u636e\u7684\u6da8\u843d\u3001\u5efa\u6a21\u65b9\u6cd5\u548c\u57ce\u5e02\u89c4\u6a21\u4e3a\u91cd\u5c3e\u5206\u5e03\u7684\u4e8b\u5b9e\u5bf9\uff08a\uff09~\uff08c\uff09\u7684\u7ed3\u679c\u662f\u81f3\u5173\u91cd\u8981\u7684\u3002<\/span><\/p>\n

\"\u96c6\u667a\"
<\/span>One of the most celebrated findings in complex systems in the last decade is that different indexes y (e.g., patents) scale nonlinearly with the population-x of the cities in which they appear, i.e., y\uff5ex^\u03b2, \u03b2\u22601. More recently, the generality of this finding has been questioned in studies using new databases and different definitions of city boundaries. In this paper we investigate the existence of nonlinear scaling using a probabilistic framework in which fluctuations are accounted explicitly. In particular, we show that this allows not only to (a) estimate \u03b2 and confidence intervals, but also to (b) quantify the evidence in favor of \u03b2\u22601 and (c) test the hypothesis that the observations are compatible with the nonlinear scaling. We employ this framework to compare 5 different models to 15 different datasets and we find that the answers to points (a)-(c) crucially depend on the fluctuations contained in the data, on how they are modeled, and on the fact that the city sizes are heavy-tailed distributed.<\/span><\/p>\n

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 \u539f\u6587\u94fe\u63a5\uff1a <\/span>http:\/\/journals.plos.org\/plosbiology\/article?id=10.1371\/journal.pbio.1002433<\/span><\/a><\/p>\n<\/blockquote>\n


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\u590d\u6742\u7f51\u7edc\u7684\u63a7\u5236\u9700\u8981\u7ed3\u6784\u548c\u52a8\u6001<\/span><\/strong><\/span><\/p>\n

\uff08Control of complex networks requires both structure and dynamics\uff09<\/span><\/strong><\/span><\/p>\n


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Scientific Reports 6, Article number: 24456 (2016) April 19, 11:50 PM<\/span><\/p>\n<\/blockquote>\n


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BY Alexander J. Gates & Luis M. Rocha<\/span><\/p>\n<\/blockquote>\n

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(Translated by -\u5973\u5468\u745c)<\/span><\/strong><\/p>\n<\/blockquote>\n

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\"\u96c6\u667a\"
The study of network structure has uncovered signatures of the organization of complex systems. However, there is also a need to understand how to control them; for example, identifying strategies to revert a diseased cell to a healthy state, or a mature cell to a pluripotent state. Two recent methodologies suggest that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics: structural controllability and minimum dominating sets. We demonstrate that such structure-only methods fail to characterize controllability when dynamics are introduced. We study Boolean network ensembles of network motifs as well as three models of biochemical regulation: the segment polarity network in Drosophila melanogaster, the cell cycle of budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in Arabidopsis thaliana. We demonstrate that structure-only methods both undershoot and overshoot the number and which sets of critical variables best control the dynamics of these models, highlighting the importance of the actual system dynamics in determining control. Our analysis further shows that the logic of automata transition functions, namely how canalizing they are, plays an important role in the extent to which structure predicts dynamics.  <\/span><\/p>\n<\/section>\n

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\u539f\u6587\u94fe\u63a5\uff1a<\/span>http:\/\/www.nature.com\/articles\/srep24456<\/a><\/span><\/p>\n<\/section>\n<\/blockquote>\n


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\uff08Similarity of Symbol Frequency Distributions with Heavy Tails\uff09<\/span><\/strong><\/p>\n


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P<\/span>hys. Rev. X 6, 021009 April 21, 9:45 PM<\/span><\/p>\n<\/blockquote>\n

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BY Martin Gerlach, Francesc Font-Clos, and Eduardo G. Altmann<\/span><\/p>\n<\/blockquote>\n

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(Translated by -\u738b\u4e0e\u5251)<\/span><\/strong><\/p>\n<\/blockquote>\n

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A mathematical technique for comparing large symbol sets suggests that less frequently used words are mainly responsible for the evolution of the English language over the past two centuries.  <\/span><\/p>\n<\/section>\n


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<\/span>\uff08\u56fe\u7247\u5747\u6765\u81ea\u4e8e\u7f51\u7edc\uff09<\/span><\/p>\n

\u590d\u6742\u6587\u6458\u7ffb\u8bd1\u5c0f\u7ec4\u7ffb\u8bd1\u4f5c\u54c1\u96c6\u9526<\/span><\/strong><\/p>\n

\u590d\u6742\u6587\u6458\u7ffb\u8bd1\u7b2c1\u671f<\/a>
<\/p>\n

\u590d\u6742\u6587\u6458\u7ffb\u8bd1\u7b2c2\u671f<\/a><\/span><\/h1>\n

\u590d\u6742\u6587\u6458\u7ffb\u8bd1\u7b2c3\u671f<\/a><\/p>\n

\u300a\u81ea\u7136\u300b\u6742\u5fd7\u5bf9\u8bdd\u5218\u6148\u6b23\uff1a\u300a\u4e09\u4f53\u300b\u5199\u4f5c\u3001\u7075\u611f\u4ee5\u53ca\u4e2d\u56fd\u79d1\u5e7b<\/a><\/p>\n

\u5218\u5b87\u6606\u8bbf\u8c08 | \u4e2d\u56fd\u79d1\u5e7b\u4e0e\u7ffb\u8bd1\u7684\u827a\u672f<\/a><\/span><\/p>\n

– END –<\/span><\/span><\/p>\n


<\/span><\/span><\/p>\n

\n


<\/span><\/p>\n

\u96c6\u667a\u4ff1\u4e50\u90e8<\/span><\/strong><\/p>\n


<\/span><\/p>\n

\u82f1\u6587\u540d\uff1a Swarm Agents Club \uff0c\u7b80\u79f0\u201c SA Club \u201d\uff0c\u6210\u7acb\u4e8e 2008 \u5e74 \uff0c \u662f\u4e00\u4e2a\u4ece\u4e8b\u5b66\u672f\u7814\u7a76\u3001\u4eab\u53d7\u79d1\u5b66\u4e50\u8da3\u7684\u63a2\u7d22\u8005\u7684\u56e2\u4f53 \u3002\u5b83\u5021\u5bfc\u4ee5\u5e73\u7b49\u5f00\u653e\u7684\u6001\u5ea6\u3001\u79d1\u5b66\u5b9e\u8bc1\u7684\u7cbe\u795e\uff0c\u8fdb\u884c\u8de8\u5b66\u79d1\u7684\u7814\u7a76\u4e0e\u4ea4\u6d41\uff0c\u529b\u56fe\u642d\u5efa\u4e00\u4e2a\u4e2d\u56fd\u7684 \u201c \u6ca1\u6709\u56f4\u5899\u7684\u7814\u7a76\u6240 \u201d<\/strong><\/span><\/span><\/p>\n

\u96c6\u667a\u4ff1\u4e50\u90e8\u7684\u53e3\u53f7\uff1a<\/span><\/strong><\/p>\n

\u8ba9\u82f9\u679c\u7838\u5f97\u66f4\u731b\u70c8\u4e9b\u5427\uff01\uff01\uff01<\/p>\n

\u96c6\u667a\u4ff1\u4e50\u90e8\u7684\u4f7f\u547d\uff08\u5373\u76ee\u6807\uff09\uff1a<\/span><\/strong><\/p>\n

\u8425\u9020\u81ea\u7531\u4ea4\u6d41\u5b66\u672f\u601d\u60f3\u7684\u5c0f\u751f\u5883\uff0c<\/p>\n

 \u5b55\u80b2\u5f00\u521b\u6027\u7684\u79d1\u5b66\u53d1\u73b0<\/p>\n

\u96c6\u667a\u6bcf\u5468\u7cbe\u54c1\u63a8\u8350<\/span><\/strong>
<\/p>\n

\u6df1\u5ea6\u5b66\u4e60\u5f81\u670d\u590d\u6742\u6027 | \u6df1\u5ea6\u201c\u6df1\u5ea6\u5b66\u4e60\u201d\u8bfb\u4e66\u4f1a\u7b2c1\u671f\u7b14\u8bb0\u968f\u60f3<\/a><\/p>\n

\u535a\u7269\u5b66\u89c6\u89d2\u4e0b\u7684\u8fdb\u5316\u3001\u71b5\u4e0e\u81ea\u7136\u79e9\u5e8f<\/a><\/p>\n

\u5982\u4f55\u638c\u63e1\u5b87\u5b99\uff1f| \u51ef\u98ce-\u96c6\u667a\u7814\u8bfb\u8425\u7b14\u8bb0\u4e4b\u4e00<\/a><\/span><\/p>\n


<\/p>\n

\"\u96c6\u667a\"<\/p>\n

\u8ba9\u82f9\u679c\u7838\u5f97\u66f4\u731b\u70c8\u4e9b\u5427\uff01<\/span><\/p>\n

\n
\"\u96c6\u667a\"
<\/section>\n<\/section>\n

\u957f\u6309\u8bc6\u522b\u4e8c\u7ef4\u7801\uff0c\u5173\u6ce8\u96c6\u667aClub\uff0c<\/span><\/p>\n

\u8ba9\u6211\u4eec\u79bb\u79d1\u5b66\u63a2\u7d22\u66f4\u8fd1\u4e00\u6b65\u3002<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n

\n

\u59cb\u53d1\u4e8e\u5fae\u4fe1\u516c\u4f17\u53f7\uff1a \u96c6\u667a\u4ff1\u4e50\u90e8 <\/p>\n<\/blockquote>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

\u5982\u4f55\u8fd0\u7528\u7f51\u7edc\u7684\u529b\u91cf\u7b80\u5316\u590d\u6742\u7cfb\u7edf\uff1f \u4ec0\u4e48\u662f\u6807\u5ea6\u975e\u7ebf\u6027\uff1f \u5982\u4f55\u63a7\u5236\u590d\u6742\u7cfb\u7edf\uff1f \u96c6\u667a\u590d\u6742\u6587\u6458\u7ffb\u8bd1\u5c0f\u7ec4\u4e3a\u4f60\u5448\u73b0\u65b0\u9c9c\u7684\u7814\u7a76\u5185\u5bb9\uff01 \u4e13\u9898\uff1a\u542b\u65f6\u7f51\u7edc\u7406\u8bba\u53ca\u5e94\u7528  \uff08Modern temporal network theory: a colloquium\uff09 Eur. Phys. J. B (2015) 8…<\/p>\n","protected":false},"author":1,"featured_media":8419,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[22],"tags":[],"special":[],"_links":{"self":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/8418"}],"collection":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=8418"}],"version-history":[{"count":1,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/8418\/revisions"}],"predecessor-version":[{"id":8430,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/8418\/revisions\/8430"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/8419"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8418"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8418"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8418"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=8418"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}