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Fast methods for finding significant motifs on labelled multi-relational networks
No full textA labelled multi-relational network (or labelled multigraph, for short) is one in which nodes have labels and a pair of nodes may be connected by an edge with one or more labels. For example, in an airline route database, 'large European city' may be the label on the Paris node and 'large Asian city' may be the label on the New Delhi node and the edge between the two cities may be labelled by several carriers. This article presents an analytical method to compute the p-values of labelled subgraph (sub-network) motifs in such labelled multi-relational networks (multigraphs). The method (and a fast approximation to the method) works for both directed and undirected graphs and extends to large subgraphs. We have validated these methods on a dataset of medium size real networks (up to tens of thousands of nodes and hundreds of thousands of edges) of different types (biological, infrastructural and collaboration networks). The pure analytical model is faster than a randomized simulation model by a factor of approximately 1000 in most of our experiments. This improvement in performance is greater for larger graphs. The approximate analytical model avoids the calculations of statistical variance and achieves nearly the same precision and recall as the pure analytical model while being several times faster. To test the scalability of our methods, we run our algorithms on synthetic and real datasets from protein-protein interaction networks, airline flight paths, the internet infrastructural network and the IMDB movie network. We also illustrate a use case of this form of analysis on a large relationship network of people involved in the Panama papers scandal, retrieving frequently used money laundering patterns. labelled multigraphs motif enumeration; motif statistical significance; random network models; multi-relational networks; multigraphs
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Motif Finding Algorithms: A Performance Comparison
No full textNetwork motifs are subgraphs of a network that occur more frequently than expected, according to some reasonable null model. They represent building blocks of complex systems such as genetic interaction networks or social networks and may reveal intriguing typical but perhaps unexpected relationships between interacting entities. The identification of network motif is a time consuming task since it subsumes the subgraph matching problem. Several algorithms have been proposed in the literature. In this paper we aim to review the motif finding problem through a systematic comparison of state-of-the-art algorithms on both real and artificial networks of different sizes. We aim to provide readers a complete overview of the performance of the various tools. As far as we know, this is the most comprehensive experimental review of motif finding algorithms to date, with respect both to the number of compared tools and to the variety and size of networks used for the experiments
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
APPAGATO: an APproximate PArallel and stochastic GrAph querying TOol for biological networks
Get PDFMotivation: Biological network querying is a problem requiring a considerable computational effort tobe solved. Given a target and a query network, it aims to find occurrences of the query in the target byconsidering topological and node similarities (i.e. mismatches between nodes, edges, or node labels).Querying tools that deal with similarities are crucial in biological network analysis since they providemeaningful results also in case of noisy data. In addition, since the size of available networks increasessteadily, existing algorithms and tools are becoming unsuitable. This is rising new challenges for the designof more efficient and accurate solutions.Results: This paper presents APPAGATO, a stochastic and parallel algorithm to find approximateoccurrences of a query network in biological networks. APPAGATO handles node, edge, and node labelmismatches. Thanks to its randomic and parallel nature, it applies to large networks and, compared toexisting tools, it provides higher performance as well as statistically significant more accurate results.Tests have been performed on protein-protein interaction networks annotated with synthetic and real geneontology terms. Case studies have been done by querying protein complexes among different species andtissue
APPAGATO: an APproximate PArallel and stochastic GrAph querying TOol for biological networks
No full textMotivation: Biological network querying is a problem requiring a considerable computational effort tobe solved. Given a target and a query network, it aims to find occurrences of the query in the target byconsidering topological and node similarities (i.e. mismatches between nodes, edges, or node labels).Querying tools that deal with similarities are crucial in biological network analysis since they providemeaningful results also in case of noisy data. In addition, since the size of available networks increasessteadily, existing algorithms and tools are becoming unsuitable. This is rising new challenges for the designof more efficient and accurate solutions.Results: This paper presents APPAGATO, a stochastic and parallel algorithm to find approximateoccurrences of a query network in biological networks. APPAGATO handles node, edge, and node labelmismatches. Thanks to its randomic and parallel nature, it applies to large networks and, compared toexisting tools, it provides higher performance as well as statistically significant more accurate results.Tests have been performed on protein-protein interaction networks annotated with synthetic and real geneontology terms. Case studies have been done by querying protein complexes among different species andtissue
Fast analytical methods for finding significant labeled graph motifs
No full textNetwork motif discovery is the problem of finding subgraphs of a network that occur more frequently than expected, according to some reasonable null hypothesis. Such subgraphs may indicate small scale interaction features in genomic interaction networks or intriguing relationships involving actors or a relationship among airlines.When nodes are labeled, they can carry information such as the genomic entity under study or the dominant genre of an actor. For that reason, labeled subgraphs convey information beyond structure and could therefore enjoy more applications. To identify statistically significant motifs in a given network, we propose an analytical method (i.e. simulation-free) that extends the works of Picard et al. (J Comput Biol 15(1):1–20, 2008) and Schbath et al. (J Bioinform Syst Biol 2009(1):616234, 2009) to label-dependent scale-free graph models. We provide an analytical expression of the mean and variance of the count under the Expected Degree Distribution random graph model. Our model deals with both induced and non-induced motifs. We have tested our methodology on a wide set of graphs ranging from protein–protein interaction networks to movie networks. The analytical model is a fast (usually faster by orders of magnitude) alternative to simulation. This advantage increases as graphs grow in size
Fast Subgraph Matching Strategies Based on Pattern-Only Heuristics
No full textMany scientific applications entail solving the subgraph isomorphism problem, i.e., given an input pattern graph, find all the subgraphs of a (usually much larger) target graph that are structurally equivalent to that input. Because subgraph isomorphism is NP-complete, methods to solve it have to use heuristics. This work evaluates subgraph isomorphism methods to assess their computational behavior on a wide range of synthetic and real graphs. Surprisingly, our experiments show that, among the leading algorithms, certain heuristics based only on pattern graphs are the most efficient
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