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construction of linear sized tolerant networks","{\"openalex\":\"W1998400388\",\"doi\":\"10.1016/0012-365x(88)90189-6\"}","2026-05-20T10:43:59.19678061","2026-05-20T10:43:59.196897827","doi","10.1016/0012-365x(88)90189-6","arxivId","openalexId","W1998400388","abstractText","publishedDate","1988-12-01","publishedYear",1988,"venue","Discrete Mathematics",336,"authors","keywords",[190,191,192],"pdfUrl","topVenue","Interconnection Networks and Systems","Advanced Graph Theory Research","Graph theory and applications","paper_W2901284226","Quasi-random graphs","{\"openalex\":\"W2901284226\",\"doi\":\"10.1007/bf02125347\"}","2026-05-16T06:49:52.238636451","2026-05-19T03:22:53.473241967","10.1007/bf02125347","W2901284226","1989-12-01",1989,"COMBINATORICA",484,[205,191,192],"Limits and Structures in Graph Theory","paper_W638149299","Complex Graphs and Networks","{\"openalex\":\"W638149299\",\"doi\":\"10.1090/cbms/107\"}","2026-05-15T03:56:37.247002465","2026-05-21T02:51:18.182558401","10.1090/cbms/107","W638149299","Graph theory in the information age Old and new concentration inequalities A generative model--the preferential attachment scheme Duplication models for biological networks Random graphs with given expected degrees The rise of the giant component Average distance and the diameter Eigenvalues of the adjacency matrix of $G(\\mathbf{w})$ The semi-circle law for $G(\\mathbf{w})$ Coupling on-line and off-line analyses of random graphs The configuration model for power law graphs The small world phenomenon in hybrid graphs Bibliography Index.","2006-08-28",2006,"Regional conference series in mathematics",715,[219,220,221],"Complex Network Analysis Techniques","Opinion Dynamics and Social Influence","Topological and Geometric Data Analysis","paper_W2027377866","The average distances in random graphs with given expected degrees","{\"openalex\":\"W2027377866\",\"doi\":\"10.1073/pnas.252631999\"}","2026-05-14T17:14:28.21362112","2026-05-21T21:20:35.205488016","10.1073/pnas.252631999","W2027377866","Random graph theory is used to examine the \"small-world phenomenon\"; any two strangers are connected through a short chain of mutual acquaintances. We will show that for certain families of random graphs with given expected degrees the average distance is almost surely of order log nlog d, where d is the weighted average of the sum of squares of the expected degrees. Of particular interest are power law random graphs in which the number of vertices of degree k is proportional to 1kbeta for some fixed exponent beta. For the case of beta > 3, we prove that the average distance of the power law graphs is almost surely of order log nlog d. However, many Internet, social, and citation networks are power law graphs with exponents in the range 2 < beta < 3 for which the power law random graphs have average distance almost surely of order log log n, but have diameter of order log n (provided having some mild constraints for the average distance and maximum degree). In particular, these graphs contain a dense subgraph, which we call the core, having n(clog log n) vertices. Almost all vertices are within distance log log n of the core although there are vertices at distance log n from the core.","2002-12-04",2002,"Proceedings of the National Academy of Sciences",918,[219,205,191],"paper_W2097147952","A random graph model for massive graphs","{\"openalex\":\"W2097147952\",\"doi\":\"10.1145/335305.335326\"}","2026-05-14T16:47:07.476223492","2026-05-21T20:11:49.196859435","10.1145/335305.335326","W2097147952","We propose a random graph model which is a special case of sparse random graphs with given degree sequences. This model involves only a small number of parameters, called logsize and log-log growth rate. These parameters capture some universal characteristics of massive graphs. Furthermore, from these parameters, various properties of the graph can be derived. For example, for certain ranges of the parameters, we will compute the expected distribution of the sizes of the connected components which almost surely occur with high probability. We will illustrate the consistency of our model with the behavior of some massive graphs derived from data in telecommunications. We will also discuss the threshold function, the giant component, and the evolution of random graphs in this model. 1 Introduction Is the World Wide Web completely connected? If not, how big is the largest component, the second largest component, etc.? Anyone who has "surfed" the Web for any length of time will come away ...","2000-05-01",2000,933,[219,191,205],"paper_W2112976607","Connected Components in Random Graphs with Given Expected Degree Sequences","{\"openalex\":\"W2112976607\",\"doi\":\"10.1007/pl00012580\"}","2026-05-14T15:44:01.358229643","2026-05-21T19:03:27.18238114","10.1007/pl00012580","W2112976607","2002-11-01","Annals of Combinatorics",960,[205,191,192],"paper_W2086254934","Local Graph Partitioning using PageRank Vectors","{\"openalex\":\"W2086254934\",\"doi\":\"10.1109/focs.2006.44\"}","2026-05-14T14:59:23.223923279","2026-05-21T18:30:18.180818236","10.1109/focs.2006.44","W2086254934","A local graph partitioning algorithm finds a cut near a specified starting vertex, with a running time that depends largely on the size of the small side of the cut, rather than the size of the input graph. In this paper, we present a local partitioning algorithm using a variation of PageRank with a specified starting distribution. We derive a mixing result for PageRank vectors similar to that for random walks, and show that the ordering of the vertices produced by a PageRank vector reveals a cut with small conductance. In particular, we show that for any set C with conductance Phi and volume k, a PageRank vector with a certain starting distribution can be used to produce a set with conductance (O(radic(Phi log k)). We present an improved algorithm for computing approximate PageRank vectors, which allows us to find such a set in time proportional to its size. In particular, we can find a cut with conductance at most oslash, whose small side has volume at least 2 ^b in time O(2 log m/(2 ^b log ² m/oslash ² ) where m is the number of edges in the graph. By combining small sets found by this local partitioning algorithm, we obtain a cut with conductance oslash and approximately optimal balance in time O(m log ⁴ m/oslash)","2006-01-01",981,[269,270,271],"Algorithms and Data Compression","Complexity and Algorithms in Graphs","VLSI and FPGA Design Techniques","paper_W2063273637","Optical orthogonal codes: design, analysis and applications","{\"openalex\":\"W2063273637\",\"doi\":\"10.1109/18.30982\"}","2026-05-14T11:28:54.317840609","2026-05-21T15:07:13.185960156","10.1109/18.30982","W2063273637","An optical orthogonal code (OOC) is a family of","1989-05-01","IEEE Transactions on Information Theory",1120,[284,285],"graph theory and CDMA systems","Coding theory and cryptography","paper_W2116810533","Spectral grouping using the nystrom method","{\"openalex\":\"W2116810533\",\"doi\":\"10.1109/tpami.2004.1262185\"}","2026-05-14T08:10:04.37018861","2026-05-21T11:24:04.183551547","10.1109/tpami.2004.1262185","W2116810533","Spectral graph theoretic methods have recently shown great promise for the problem of image segmentation. However, due to the computational demands of these approaches, applications to large problems such as spatiotemporal data and high resolution imagery have been slow to appear. The contribution of this paper is a method that substantially reduces the computational requirements of grouping algorithms based on spectral partitioning making it feasible to apply them to very large grouping problems. Our approach is based on a technique for the numerical solution of eigenfunction problems known as the Nyström method. This method allows one to extrapolate the complete grouping solution using only a small number of samples. In doing so, we leverage the fact that there are far fewer coherent groups in a scene than pixels.","2004-02-01",2004,"IEEE Transactions on Pattern Analysis and Machine Intelligence",1340,[221,300,301],"https://escholarship.org/content/qt29z29233/qt29z29233.pdf?t=lnpvil","Remote-Sensing Image Classification","Medical Image Segmentation Techniques","paper_W1578099820","Spectral Graph Theory","{\"openalex\":\"W1578099820\",\"doi\":\"10.1090/cbms/092\"}","2026-05-14T01:15:41.326826581","2026-05-21T02:57:07.207259707","10.1090/cbms/092","W1578099820","Eigenvalues and the Laplacian of a graph Isoperimetric problems Diameters and eigenvalues Paths, flows, and routing Eigenvalues and quasi-randomness Expanders and explicit constructions Eigenvalues of symmetrical graphs Eigenvalues of subgraphs with boundary conditions Harnack inequalities Heat kernels Sobolev inequalities Advanced techniques for random walks on graphs Bibliography Index.","1996-12-03",1996,5765,[205,192,191],{"_60":315,"_62":316},[],[317],{"_65":318,"_67":319,"_69":320,"_71":321},1780512621867,{"_74":323,"_76":77,"_78":318,"_79":-5,"_80":26,"_81":26,"_82":26,"_83":-5,"_84":-5,"_85":86,"_35":87,"_88":89},[73,322],"[\"portal\",\"industries\"]","industries",[324,325,326,327,328,329,330,331,332,333,334,335],{"_15":397,"_29":398,"_31":-5,"_32":399,"_33":400,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":339,"_42":-5,"_44":-5,"_340":341,"_342":-5,"_343":77,"_344":345,"_346":401,"_348":26,"_349":86,"_350":26,"_351":-5,"_58":398,"_59":400},{"_15":393,"_29":394,"_31":-5,"_32":-5,"_33":395,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":339,"_42":-5,"_44":-5,"_340":341,"_342":-5,"_343":77,"_344":345,"_346":396,"_348":26,"_349":86,"_350":26,"_351":-5,"_58":394,"_59":395},{"_15":387,"_29":388,"_31":-5,"_32":389,"_33":390,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":339,"_42":-5,"_44":-5,"_340":341,"_342":-5,"_343":77,"_344":345,"_346":391,"_348":26,"_349":86,"_350":26,"_351":-5,"_58":388,"_59":390},{"_15":383,"_29":384,"_31":-5,"_32":-5,"_33":385,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":339,"_42":-5,"_44":-5,"_340":341,"_342":-5,"_343":77,"_344":345,"_346":386,"_348":26,"_349":86,"_350":26,"_351":-5,"_58":384,"_59":385},{"_15":379,"_29":380,"_31":-5,"_32":-5,"_33":381,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":339,"_42":-5,"_44":-5,"_340":341,"_342":-5,"_343":77,"_344":345,"_346":382,"_348":26,"_349":86,"_350":26,"_351":-5,"_58":380,"_59":381},{"_15":373,"_29":374,"_31":-5,"_32":375,"_33":376,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":339,"_42":-5,"_44":-5,"_340":341,"_342":-5,"_343":77,"_344":345,"_346":377,"_348":26,"_349":86,"_350":26,"_351":-5,"_58":374,"_59":376},{"_15":368,"_29":369,"_31":-5,"_32":-5,"_33":370,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":339,"_42":-5,"_44":-5,"_340":341,"_342":-5,"_343":77,"_344":345,"_346":371,"_348":26,"_349":372,"_350":26,"_351":-5,"_58":369,"_59":370},{"_15":364,"_29":365,"_31":-5,"_32":-5,"_33":366,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":339,"_42":-5,"_44":-5,"_340":341,"_342":-5,"_343":77,"_344":345,"_346":367,"_348":26,"_349":86,"_350":26,"_351":-5,"_58":365,"_59":366},{"_15":360,"_29":361,"_31":-5,"_32":-5,"_33":362,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":339,"_42":-5,"_44":-5,"_340":341,"_342":-5,"_343":77,"_344":345,"_346":363,"_348":26,"_349":86,"_350":26,"_351":-5,"_58":361,"_59":362},{"_15":356,"_29":357,"_31":-5,"_32":-5,"_33":358,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":339,"_42":-5,"_44":-5,"_340":341,"_342":-5,"_343":77,"_344":345,"_346":359,"_348":26,"_349":86,"_350":26,"_351":-5,"_58":357,"_59":358},{"_15":352,"_29":353,"_31":-5,"_32":-5,"_33":354,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":339,"_42":-5,"_44":-5,"_340":341,"_342":-5,"_343":77,"_344":345,"_346":355,"_348":26,"_349":86,"_350":26,"_351":-5,"_58":353,"_59":354},{"_15":336,"_29":337,"_31":-5,"_32":-5,"_33":338,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":339,"_42":-5,"_44":-5,"_340":341,"_342":-5,"_343":77,"_344":345,"_346":347,"_348":26,"_349":86,"_350":26,"_351":-5,"_58":337,"_59":338},"tax_ind_smartphone","智能手机","Smartphone","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/智能手机","sortOrder","isLeaf","entityCount","children","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算",true,"tax_ind_blockchain","区块链",[378],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[392],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[402,403],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_406":24},"SITE_BASE_URL"]

Fan Chung 人物

Fan Chung · 相关文章