the decisions individuals make. These networks link different types of
entities, for example, people and the events they attend. Network analysis
algorithms usually project such networks unto simple graphs composed of
entities of a single type. In the process, they conflate relations between
entities of different types and loose important structural information. We
develop a mathematical framework that can be used to compactly represent and
analyze heterogeneous networks that combine multiple entity and link types. We
generalize Bonacich centrality, which measures connectivity between nodes by
the number of paths between them, to heterogeneous networks and use this
measure to study network structure. Specifically, we extend the popular
modularity-maximization method for community detection to use this centrality
metric. We also rank nodes based on their connectivity to other nodes. One
advantage of this centrality metric is that it has a tunable parameter we can
use to set the length scale of interactions. By studying how rankings change
with this parameter allows us to identify important nodes in the network. We
apply the proposed method to analyze the structure of several heterogeneous
networks. We show that exploiting additional sources of evidence corresponding
to links between, as well as among, different entity types yields new insights
into network structure.
ER - TY - JOUR AU - Hanhijärvi, Sami AU - Garriga, Gemma AU - Puolamäki, Kai T1 - Randomization techniques for graphs JO - PY - 2009/ VL - IS - SP - EP - UR - http://eprints.pascal-network.org/archive/00004486/ DO - KW - graphs KW - randomization KW - toRead L1 - SN - N1 - Scientific Commons: Randomization techniques for graphs (2009), 2009 [Hanhijärvi, Sami, Garriga, Gemma, Puolamäki, Kai] N1 - AB - Mining graph data is an active research area. Several data mining methods and algorithms have been proposed to identify structures from graphs; still, the evaluation of those results is lacking. Within the framework of statistical hypothesis testing, we focus in this paper on randomization techniques for unweighted undirected graphs. Randomization is an important approach to assess the statistical significance of data mining results. Given an input graph, our randomization method will sample data from the class of graphs that share certain structural properties with the input graph. Here we describe three alternative algorithms based on local edge swapping and Metropolis sampling. We test our framework with various graph data sets and mining algorithms for two applications, namely graph clustering and frequent subgraph mining. ER - TY - CONF AU - Mirowski, Piotr W. AU - LeCun, Yann A2 - Buntine, Wray L. A2 - Grobelnik, Marko A2 - Mladenic, Dunja A2 - Shawe-Taylor, John T1 - Dynamic Factor Graphs for Time Series Modeling. T2 - ECML/PKDD (2) PB - Springer C1 - PY - 2009/ CY - VL - 5782 IS - SP - 128 EP - 143 UR - http://dblp.uni-trier.de/db/conf/pkdd/pkdd2009-2.html#MirowskiL09 DO - KW - 2009 KW - ecml KW - factor KW - graphs KW - pkdd KW - series KW - time L1 - SN - 978-3-642-04173-0 N1 - N1 - AB - ER - TY - CONF AU - Zhu, Feida AU - Chen, Chen AU - Yan, Xifeng AU - Han, Jiawei AU - Yu, Philip S A2 - T1 - Graph OLAP: Towards Online Analytical Processing on Graphs T2 - Proc. 2008 Int. Conf. on Data Mining (ICDM'08), Pisa, Italy, Dec. 2008. PB - C1 - PY - 2008/12 CY - VL - IS - SP - EP - UR - DO - KW - graph KW - graphs KW - olap KW - sna L1 - SN - N1 - Resource: Graph OLAP: Towards Online Analytical Processing on Graphs N1 - AB - ER - TY - JOUR AU - Baeza-Yates, Ricardo T1 - Graphs from Search Engine Queries JO - SOFSEM 2007: Theory and Practice of Computer Science PY - 2007/ VL - 4362 IS - SP - 1 EP - 8 UR - http://dx.doi.org/10.1007/978-3-540-69507-3_1 DO - KW - sofsem2007 KW - baeza_yates KW - query_log_mining KW - graphs L1 - SN - N1 - SpringerLink - Book Chapter N1 - AB - Server logs of search engines store traces of queries submitted by users, which include queries themselves along with Webpages selected in their answers. Here we describe several graph-based relations among queries and many applications wherethese graphs could be used. ER - TY - GEN AU - A2 - Dau, Frithjof A2 - Mugnier, Marie-Laure A2 - Stumme, Gerd T1 - Conceptual Structures: Common Semantics for Sharing Knowledge, 13th International Conference on Conceptual Structures, ICCS 2005, Kassel, Germany, July 17-22, 2005, Proceedings JO - PB - Springer C1 - PY - 2005/ VL - 3596 IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/conf/iccs05 DO - KW - 2005 KW - Germany KW - Kassel KW - analysis KW - concept KW - conceptual KW - conference KW - fca KW - formal KW - graphs KW - iccs KW - knowledge KW - l3s KW - myown KW - proceedings KW - sharing KW - structures L1 - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - GEN AU - A2 - Dau, Frithjof A2 - Mugnier, Marie-Laure A2 - Stumme, Gerd T1 - Contributions to ICCS 2005 JO - PB - kassel university press C1 - Kassel PY - 2005/ VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/conf/iccs05 DO - KW - 2005 KW - Germany KW - Kassel KW - analysis KW - concept KW - conceptual KW - conference KW - fca KW - formal KW - graphs KW - iccs KW - knowledge KW - l3s KW - myown KW - proceedings KW - sharing KW - structures L1 - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - JOUR AU - Blondel, Vincent D. AU - Gajardo, Anah AU - Heymans, Maureen AU - Senellart, Pierre AU - Dooren, Paul Van T1 - A Measure of Similarity between Graph Vertices: Applications to Synonym Extraction and Web Searching JO - SIAM Rev. PY - 2004/ VL - 46 IS - 4 SP - 647 EP - 666 UR - http://portal.acm.org/citation.cfm?id=1035533.1035557 DO - http://dx.doi.org/10.1137/S0036144502415960 KW - detect KW - graphs KW - similarity KW - synonymy L1 - SN - N1 - A Measure of Similarity between Graph Vertices N1 - AB - We introduce a concept of similarity between vertices of directed graphs. Let GA and GB be two directed graphs with, respectively, nA and nB vertices. We define an nB times nA similarity matrix S whose real entry sij expresses how similar vertex j (in GA) is to vertex i (in GB): we say that sij is their similarity score. The similarity matrix can be obtained as the limit of the normalized even iterates of Sk+1 = BSkAT + BTSkA, where A and B are adjacency matrices of the graphs and S0 is a matrix whose entries are all equal to 1. In the special case where GA = GB = G, the matrix S is square and the score sij is the similarity score between the vertices i and j of G. We point out that Kleinberg's "hub and authority" method to identify web-pages relevant to a given query can be viewed as a special case of our definition in the case where one of the graphs has two vertices and a unique directed edge between them. In analogy to Kleinberg, we show that our similarity scores are given by the components of a dominant eigenvector of a nonnegative matrix. Potential applications of our similarity concept are numerous. We illustrate an application for the automatic extraction of synonyms in a monolingual dictionary. ER - TY - JOUR AU - Newman, M. E. J. T1 - Analysis of weighted networks JO - Phys. Rev. E PY - 2004/november VL - 70 IS - 5 SP - EP - UR - http://pre.aps.org/abstract/PRE/v70/i5/e056131 DO - 10.1103/PhysRevE.70.056131 KW - COMMUNE KW - community KW - detection KW - graphs KW - modularity KW - networks KW - weighted L1 - SN - N1 - N1 - AB - ER - TY - GEN AU - A2 - Delugach, H. A2 - Stumme, G. T1 - Conceptual Structures -- Broadening the Base. Proc. 9th International Conference on Conceptual Structures JO - PB - Springer C1 - Heidelberg PY - 2001/ VL - 2120 IS - SP - EP - UR - DO - KW - 2001 KW - analysis KW - cg KW - cgs KW - concept KW - conceptual KW - fca KW - formal KW - graphs KW - iccs KW - myown KW - structures L1 - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Eklund, P. AU - Groh, B. AU - Stumme, G. AU - Wille, R. A2 - Ganter, B. A2 - Mineau, G. W. T1 - Contextual-Logic Extension of TOSCANA. T2 - Conceptual Structures: Logical, Linguistic, and Computational PB - Springer C1 - Heidelberg PY - 2000/ CY - VL - 1867 IS - SP - 453 EP - 467 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2000/ICCS_toscanaextension.pdf DO - KW - 2000 KW - analysis KW - cg KW - cgs KW - concept KW - conceptual KW - fca KW - formal KW - graph KW - graphs KW - iccs KW - myown KW - toscana L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - JOUR AU - Stumme, G. T1 - 8th International Conference on Conceptual Structures. Conference Report JO - Knowledge Organization PY - 2000/ VL - 27 IS - 3 SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2000/ConferenceReportICCS00.pdf DO - KW - 2000 KW - analysis KW - cg KW - concept KW - conceptual KW - conference KW - fcacgs KW - formal KW - graphs KW - iccs KW - lattices KW - myown KW - report KW - structures L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - GEN AU - A2 - Stumme, G. T1 - Working with Conceptual Structures -- Contributions to ICCS 2000. Suppl. Proc. 8th International
Conference on Conceptual Structures (ICCS 2000) JO - PB - Shaker C1 - Aachen PY - 2000/ VL - IS - SP - EP - UR - DO - KW - 2000 KW - analysis KW - cg KW - cgs KW - concept KW - conceptual KW - conference KW - fca KW - formal KW - graphs KW - iccs KW - myown KW - proceedings KW - structures L1 - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Mineau, Guy AU - Stumme, Gerd AU - Wille, Rudolf A2 - Tepfenhart, W. A2 - Cyre, W. T1 - Conceptual Structures Represented by Conceptual Graphs and Formal Concept Analysis T2 - Conceptual Structures: Standards and Practices. Proc. ICCS '99 PB - Springer C1 - Heidelberg PY - 1999/ CY - VL - 1640 IS - SP - 423 EP - 441 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/1999/ICCS99.pdf DO - KW - 1999 KW - analysis KW - cg KW - cgs KW - concept KW - conceptual KW - fca KW - formal KW - graphs KW - iccs KW - knowledge KW - myown KW - representation KW - structures L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - CONF AU - Wille, Rudolf A2 - Lukose, D. A2 - Delugach, H. A2 - Keeler, M. A2 - Searle, L. A2 - Sowa, J. F. T1 - Conceptual Graphs and Formal Concept Analysis T2 - Conceptual Structures: Fulfilling Peirce's Dream PB - Springer C1 - Heidelberg PY - 1997/ CY - VL - 1257 IS - SP - 290 EP - 303 UR - DO - KW - ag1 KW - analysis KW - begriffsanalyse KW - cg KW - concept KW - conceptual KW - darmstadt KW - fba KW - fca KW - formal KW - graph KW - graphs L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Chein, Michel AU - Mugnier, Marie-Laure T1 - Conceptual graphs: fundamental notions JO - Revue d'Intelligence Artificielle PY - 1992/ VL - 6 IS - SP - 365 EP - 406 UR - DO - KW - conceptual KW - fundamental KW - graphs KW - notions L1 - SN - N1 - N1 - AB - Nous définissons précisément les notions de base du modèle des graphes conceptuels de Sowa [Sowa 84] et en étudions les propriétés. Nos résultats portent principalement sur la structure de la relation de spécialisation, la correspondance entre opérations de graphes et opérations logiques, et la complexité algorithmique de la mise en œuvre du modèle ER - TY - BOOK AU - Sowa, J. F. A2 - T1 - Conceptual Structures: Information Processing in Mind and Machine PB - Addison-Wesley Publishing Company C1 - Reading, MA PY - 1984/ VL - IS - SP - EP - UR - DO - KW - cg KW - cgs KW - conceptual KW - graphs KW - information KW - structures L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Erdős, Pal AU - Rényi, Alfréd T1 - On Random Graphs JO - Publications Mathematicae PY - 1959/ VL - 6 IS - SP - EP - UR - DO - KW - analysis KW - graphs KW - network KW - random L1 - SN - N1 - N1 - AB - ER -