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  • 1
    Online Resource
    Online Resource
    On power-law relationships of the Internet topology
    Association for Computing Machinery (ACM) ; 1999
    In:  ACM SIGCOMM Computer Communication Review Vol. 29, No. 4 ( 1999-10), p. 251-262
    Details
    In: ACM SIGCOMM Computer Communication Review, Association for Computing Machinery (ACM), Vol. 29, No. 4 ( 1999-10), p. 251-262
    Abstract: Despite the apparent randomness of the Internet, we discover some surprisingly simple power-laws of the Internet topology. These power-laws hold for three snapshots of the Internet, between November 1997 and December 1998, despite a 45% growth of its size during that period. We show that our power-laws fit the real data very well resulting in correlation coefficients of 96% or higher.Our observations provide a novel perspective of the structure of the Internet. The power-laws describe concisely skewed distributions of graph properties such as the node outdegree. In addition, these power-laws can be used to estimate important parameters such as the average neighborhood size, and facilitate the design and the performance analysis of protocols. Furthermore, we can use them to generate and select realistic topologies for simulation purposes.
    Type of Medium: Online Resource
    ISSN: 0146-4833
    URL: Article
    DOI: 10.1145/316194.316229
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 1999
    detail.hit.zdb_id: 188525-X
    detail.hit.zdb_id: 2025907-4
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Threshold conditions for arbitrary cascade models on arbitrary networks
    Springer Science and Business Media LLC ; 2012
    In:  Knowledge and Information Systems Vol. 33, No. 3 ( 2012-12), p. 549-575
    Details
    In: Knowledge and Information Systems, Springer Science and Business Media LLC, Vol. 33, No. 3 ( 2012-12), p. 549-575
    Type of Medium: Online Resource
    ISSN: 0219-1377 , 0219-3116
    URL: Article
    DOI: 10.1007/s10115-012-0520-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2012
    detail.hit.zdb_id: 2023541-0
    detail.hit.zdb_id: 2036569-X
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Competing Memes Propagation on Networks: A Network Science Perspective
    Institute of Electrical and Electronics Engineers (IEEE) ; 2013
    In:  IEEE Journal on Selected Areas in Communications Vol. 31, No. 6 ( 2013-06), p. 1049-1060
    Details
    In: IEEE Journal on Selected Areas in Communications, Institute of Electrical and Electronics Engineers (IEEE), Vol. 31, No. 6 ( 2013-06), p. 1049-1060
    Type of Medium: Online Resource
    ISSN: 0733-8716
    URL: Article
    DOI: 10.1109/JSAC.2013.130607
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2013
    detail.hit.zdb_id: 2029371-9
    detail.hit.zdb_id: 605072-4
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  • 4
    Online Resource
    Online Resource
    Competing memes propagation on networks : a case study of composite networks
    Association for Computing Machinery (ACM) ; 2012
    In:  ACM SIGCOMM Computer Communication Review Vol. 42, No. 5 ( 2012-09-24), p. 5-12
    Details
    In: ACM SIGCOMM Computer Communication Review, Association for Computing Machinery (ACM), Vol. 42, No. 5 ( 2012-09-24), p. 5-12
    Abstract: If a false rumor propagates via Twitter, while the truth propagates between friends in Facebook, which one will prevail? This question captures the essence of the problem we address here. We study the intertwined propagation of two competing "memes" (or viruses, rumors, products etc.) in a composite network. A key novelty is the use of a composite network, which in its simplest model is defined as a single set of nodes with two distinct types of edges interconnecting them. Each meme spreads across the composite network in accordance to an SIS-like propagation model (a flu-like infection-recovery). To study the epidemic behavior of our system, we formulate it as a non-linear dynamic system (NLDS). We develop a metric for each meme that is based on the eigenvalue of an appropriately constructed matrix and argue that this metric plays a key role in determining the "winning" meme. First, we prove that our metric determines the tipping point at which both memes become extinct eventually. Second, we conjecture that the meme with the strongest metric will most likely prevail over the other, and we show evidence of that via simulations in both real and synthetic composite networks. Our work is among the first to study the interplay between two competing memes in composite networks.
    Type of Medium: Online Resource
    ISSN: 0146-4833
    URL: Article
    DOI: 10.1145/2378956.2378958
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2012
    detail.hit.zdb_id: 188525-X
    detail.hit.zdb_id: 2025907-4
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    Online Resource
  • 5
    Online Resource
    Online Resource
    Eigen-Optimization on Large Graphs by Edge Manipulation
    Association for Computing Machinery (ACM) ; 2016
    In:  ACM Transactions on Knowledge Discovery from Data Vol. 10, No. 4 ( 2016-07-27), p. 1-30
    Details
    In: ACM Transactions on Knowledge Discovery from Data, Association for Computing Machinery (ACM), Vol. 10, No. 4 ( 2016-07-27), p. 1-30
    Abstract: Large graphs are prevalent in many applications and enable a variety of information dissemination processes, e.g., meme, virus, and influence propagation. How can we optimize the underlying graph structure to affect the outcome of such dissemination processes in a desired way (e.g., stop a virus propagation, facilitate the propagation of a piece of good idea, etc)? Existing research suggests that the leading eigenvalue of the underlying graph is the key metric in determining the so-called epidemic threshold for a variety of dissemination models. In this paper, we study the problem of how to optimally place a set of edges (e.g., edge deletion and edge addition) to optimize the leading eigenvalue of the underlying graph, so that we can guide the dissemination process in a desired way. We propose effective, scalable algorithms for edge deletion and edge addition, respectively. In addition, we reveal the intrinsic relationship between edge deletion and node deletion problems. Experimental results validate the effectiveness and efficiency of the proposed algorithms.
    Type of Medium: Online Resource
    ISSN: 1556-4681 , 1556-472X
    URL: Article
    DOI: 10.1145/2903148
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2016
    detail.hit.zdb_id: 2257358-6
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  • 6
    Online Resource
    Online Resource
    Facebook wall posts: a model of user behaviors
    Springer Science and Business Media LLC ; 2017
    In:  Social Network Analysis and Mining Vol. 7, No. 1 ( 2017-12)
    Details
    In: Social Network Analysis and Mining, Springer Science and Business Media LLC, Vol. 7, No. 1 ( 2017-12)
    Type of Medium: Online Resource
    ISSN: 1869-5450 , 1869-5469
    URL: Article
    DOI: 10.1007/s13278-017-0422-9
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2595306-0
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  • 7
    Online Resource
    Online Resource
    RTG: a recursive realistic graph generator using random typing
    Springer Science and Business Media LLC ; 2009
    In:  Data Mining and Knowledge Discovery Vol. 19, No. 2 ( 2009-10), p. 194-209
    Details
    In: Data Mining and Knowledge Discovery, Springer Science and Business Media LLC, Vol. 19, No. 2 ( 2009-10), p. 194-209
    Type of Medium: Online Resource
    ISSN: 1384-5810 , 1573-756X
    URL: Article
    DOI: 10.1007/s10618-009-0140-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2009
    detail.hit.zdb_id: 1386325-3
    detail.hit.zdb_id: 1479890-6
    SSG: 24,1
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  • 8
    Online Resource
    Online Resource
    Protein complex identification by supervised graph local clustering
    Oxford University Press (OUP) ; 2008
    In:  Bioinformatics Vol. 24, No. 13 ( 2008-07-01), p. i250-i268
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 24, No. 13 ( 2008-07-01), p. i250-i268
    Abstract: Motivation: Protein complexes integrate multiple gene products to coordinate many biological functions. Given a graph representing pairwise protein interaction data one can search for subgraphs representing protein complexes. Previous methods for performing such search relied on the assumption that complexes form a clique in that graph. While this assumption is true for some complexes, it does not hold for many others. New algorithms are required in order to recover complexes with other types of topological structure. Results: We present an algorithm for inferring protein complexes from weighted interaction graphs. By using graph topological patterns and biological properties as features, we model each complex subgraph by a probabilistic Bayesian network (BN). We use a training set of known complexes to learn the parameters of this BN model. The log-likelihood ratio derived from the BN is then used to score subgraphs in the protein interaction graph and identify new complexes. We applied our method to protein interaction data in yeast. As we show our algorithm achieved a considerable improvement over clique based algorithms in terms of its ability to recover known complexes. We discuss some of the new complexes predicted by our algorithm and determine that they likely represent true complexes. Availability: Matlab implementation is available on the supporting website: www.cs.cmu.edu/~qyj/SuperComplex Contact:  zivbj@cs.cmu.edu
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    URL: Article
    DOI: 10.1093/bioinformatics/btn164
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2008
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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    Online Resource
  • 9
    Online Resource
    Online Resource
    On data mining, compression, and Kolmogorov complexity
    Springer Science and Business Media LLC ; 2007
    In:  Data Mining and Knowledge Discovery Vol. 15, No. 1 ( 2007-7-5), p. 3-20
    Details
    In: Data Mining and Knowledge Discovery, Springer Science and Business Media LLC, Vol. 15, No. 1 ( 2007-7-5), p. 3-20
    Type of Medium: Online Resource
    ISSN: 1384-5810 , 1573-756X
    URL: Article
    DOI: 10.1007/s10618-006-0057-3
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2007
    detail.hit.zdb_id: 1386325-3
    detail.hit.zdb_id: 1479890-6
    SSG: 24,1
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    Online Resource
  • 10
    Online Resource
    Online Resource
    Universal and Distinct Properties of Communication Dynamics : How to Generate Realistic Inter-event Times
    Association for Computing Machinery (ACM) ; 2015
    In:  ACM Transactions on Knowledge Discovery from Data Vol. 9, No. 3 ( 2015-04-13), p. 1-31
    Details
    In: ACM Transactions on Knowledge Discovery from Data, Association for Computing Machinery (ACM), Vol. 9, No. 3 ( 2015-04-13), p. 1-31
    Abstract: With the advancement of information systems, means of communications are becoming cheaper, faster, and more available. Today, millions of people carrying smartphones or tablets are able to communicate practically any time and anywhere they want. They can access their e-mails, comment on weblogs, watch and post videos and photos (as well as comment on them), and make phone calls or text messages almost ubiquitously. Given this scenario, in this article, we tackle a fundamental aspect of this new era of communication: How the time intervals between communication events behave for different technologies and means of communications. Are there universal patterns for the Inter-Event Time Distribution (IED)? How do inter-event times behave differently among particular technologies? To answer these questions, we analyzed eight different datasets from real and modern communication data and found four well-defined patterns seen in all the eight datasets. Moreover, we propose the use of the Self-Feeding Process (SFP) to generate inter-event times between communications. The SFP is an extremely parsimonious point process that requires at most two parameters and is able to generate inter-event times with all the universal properties we observed in the data. We also show three potential applications of the SFP: as a framework to generate a synthetic dataset containing realistic communication events of any one of the analyzed means of communications, as a technique to detect anomalies, and as a building block for more specific models that aim to encompass the particularities seen in each of the analyzed systems.
    Type of Medium: Online Resource
    ISSN: 1556-4681 , 1556-472X
    URL: Article
    DOI: 10.1145/2700399
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2015
    detail.hit.zdb_id: 2257358-6
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