Large Collaboration Networks Research Articles

A Variational Neural Architecture for Skill-based Team Formation

Team formation is concerned with the identification of a group of experts who have a high likelihood of effectively collaborating with each other to satisfy a collection of input skills. Solutions to this task have mainly adopted graph operations and at least have the following limitations: (1) they are computationally demanding, as they require finding shortest paths on large collaboration networks; (2) they use various types of heuristics to reduce the exploration space over the collaboration network to become practically feasible; therefore, their results are not necessarily optimal; and (3) they are not well-suited for collaboration network structures given the sparsity of these networks. Our work proposes a variational Bayesian neural network architecture that learns representations for teams whose members have collaborated with each other in the past. The learned representations allow our proposed approach to mine teams that have a past collaborative history and collectively cover the requested desirable set of skills. Through our experiments, we demonstrate that our approach shows stronger performance compared to a range of strong team formation techniques from both quantitative and qualitative perspectives.

The academic wanderer: structure of collaboration network and relation with research performance

Thanks to the widespread availability of large-scale datasets on scholarly outputs, science itself has come under the microscope with the aim of capturing a quantitative understanding of its workings. In this study, we leverage well-established cognitive models coming from anthropology in order to characterise the personal network of collaborations between scientists, i.e., the network considered from the standpoint of each individual researcher (referred to as ego network), in terms of the cognitive investment they devote to the different collaborations. Building upon these models, we study the interplay between the structure of academic collaborations, academic performance, and academic mobility at different career stages. We take into account both purely academic mobility (i.e., the number of affiliation changes) and geographical mobility (i.e., physical relocations to different countries). For our investigation, we rely on a dataset comprising the geo-referenced publications of a group of 81,500 authors extracted from Scopus, one of the biggest repositories of academic knowledge. Our main finding is that there is a clear correlation between the structure of co-authorship ego networks and academic performance indices: the more one publishes and the higher their impact, the larger their collaboration network. However, we observe a capacity bound effect, whereby, beyond a certain point, higher performances become increasingly less correlated with large collaboration networks. We also find that international academic migrants are better at growing their networks than researchers that only migrate within the same country, but the latter seem to be better in exploiting their collaboration to achieve higher impact. High academic mobility does not appear to translate into better academic performance or larger collaboration networks. This shows a different finding with respect to related literature, where scientific productivity is seen as directly linked to mobility. Our results show that, when looking at impact of research, this is not necessarily the case.

Linked Open Data Infrastructure for Digital Humanities in Finland

This paper presents and overviews Linked Open Data Infrastructure for Digital Humanities in Finland (LODI4DH), a joint initiative of Aalto University, Department of Computer Science, and University of Helsinki (UH), Helsinki Centre for Digital Humanities (HELDIG), for creating a national data infrastructure and Linked Data services for open science. The data and services enable publication and utilization of datasets for data-intensive Digital Humanities (DH) research in structured, standardized formats via open interfaces. LODI4DH is based on a large national collaboration network and software created during a long line of national projects in DH between UH and Aalto since 2003. This work has created several in-use infrastructure prototypes, such as the ONKI and Finto ontology service now at the National Library of Finland, the Linked Data Finland data publishing platform LDF.fi, and the “Sampo” series of semantic portals testing and demonstrating the usability of the approach in practical applications. Thus far, these systems have had millions of end-users on the Web suggesting a high potential of utilizing the technology and Linked Data infrastructure in DH.

Detecting topic-level influencers in large-scale scientific networks

Scientific networks play an increasingly important role in facilitating knowledge and technique diffusion. In such networks, highly influential nodes (scientists or literatures) are prone to stimulate other researchers in the generation of innovative ideas. The objective of this study is to detect topic-level influencers from a large collection of links between nodes and textual contents in scientific networks. For this purpose, we propose a sparse link topic model (SLTM) that introduces a “Spike and Slab” prior to achieve sparsity in node-topic distribution. Compared with previous approaches, our model assumes that a node usually focuses on several salient topics instead of a wide range of topics, which is useful in learning topic-level influencers in scientific networks. In addition, a collapsed variational Bayesian (CVB) inference algorithm is designed for large-scale applications. Our experiments are conducted on a large scientific collaboration network. The results reveal that the proposed model significantly improves the precision of topic-level detection. Our analysis also reflects that SLTM can explicitly model the sparse topical structure of each node in the network.

Spin states of asteroids in the Eos collisional family

Eos family was created during a catastrophic impact about 1.3 Gyr ago. Rotation states of individual family members contain information about the history of the whole population. We aim to increase the number of asteroid shape models and rotation states within the Eos collision family, as well as to revise previously published shape models from the literature. Such results can be used to constrain theoretical collisional and evolution models of the family, or to estimate other physical parameters by a thermophysical modeling of the thermal infrared data. We use all available disk-integrated optical data (i.e., classical dense-in-time photometry obtained from public databases and through a large collaboration network as well as sparse-in-time individual measurements from a few sky surveys) as input for the convex inversion method, and derive 3D shape models of asteroids together with their rotation periods and orientations of rotation axes. We present updated shape models for 15 asteroids and new shape model determinations for 16 asteroids. Together with the already published models from the publicly available DAMIT database, we compiled a sample of 56 Eos family members with known shape models that we used in our analysis of physical properties within the family. Rotation states of asteroids smaller than ∼ 20 km are heavily influenced by the YORP effect, whilst the large objects more or less retained their rotation state properties since the family creation. Moreover, we also present a shape model and bulk density of asteroid (423) Diotima, an interloper in the Eos family, based on the disk-resolved data obtained by the Near InfraRed Camera (Nirc2) mounted on the W.M. Keck II telescope.

The beauty of crystals mirrored in collaboration networks of science

Most complex systems are often random and unpredictable. But the emergence of spatial order and special symmetry in the regular shapes of crystals is a constant source of fascination for people of all over the world. In comparison, the emerging “Social Gestalts” offer the evidence that large collaboration networks are self-organizing given the high degree of spatial order and special symmetry in the regular shapes of the manifold 3-D graph images of the “Social Gestalts”. The “Social Gestalt” model is a new parametric model visualizing 3-D graphs on the level of large networks, using animation to show these graphs from different points of view. The emerging spatial (3-D) order and special symmetry in the regular shapes of both crystals and “Social Gestalts” offer a new question for interdisciplinary studies in future.

New and updated convex shape models of asteroids based on optical data from a large collaboration network

Asteroid modeling efforts in the last decade resulted in a comprehensive dataset of almost 400 convex shape models and their rotation states. This amount already provided a deep insight into physical properties of main-belt asteroids or large collisional families. We aim to increase the number of asteroid shape models and rotation states. Such results are an important input for various further studies such as analysis of asteroid physical properties in different populations, including smaller collisional families, thermophysical modeling, and scaling shape models by disk-resolved images, or stellar occultation data. This provides, in combination with known masses, bulk density estimates, but constrains also theoretical collisional and evolutional models of the Solar System. We use all available disk-integrated optical data (i.e., classical dense-in-time photometry obtained from public databases and through a large collaboration network as well as sparse-in-time individual measurements from a few sky surveys) as an input for the convex inversion method, and derive 3D shape models of asteroids, together with their rotation periods and orientations of rotation axes. The key ingredient is the support of more that one hundred observers who submit their optical data to publicly available databases. We present updated shape models for 36 asteroids, for which mass estimates are currently available in the literature or their masses will be most likely determined from their gravitational influence on smaller bodies, which orbital deflection will be observed by the ESA Gaia astrometric mission. This was achieved by using additional optical data from recent apparitions for the shape optimization. Moreover, we also present new shape model determinations for 250 asteroids, including 13 Hungarias and 3 near-Earth asteroids.

A method for the identification of collaboration in large scientific databases

The analysis of scientific collaboration networks has contributed significantly to improve the understanding of the collaboration process between researchers. Additionally, it has helped to understand how scientific productions by researchers and research groups evolve. However, the identification of collaborations in large scientific databases is not a trivial task, given the high computational cost of the prevalent methods. This paper proposes a method for identifying collaborations in large scientific databases, namely, ISColl – Identification of Scientific Collaboration. Unlike methods that use techniques such as exhaustive comparisons of publication pairs, the proposed method produces satisfactory results with a low computational cost, thus providing an interesting alternative for the modelling and characterization of large scientific collaboration networks. To demonstrate the potential of the proposed technique, tests were conducted using scientific publications data registered in the Lattes Platform of CNPq, with the obtained results yielding excellent accuracy during the identification of scientific collaborations.

Social Influence Based Clustering and Optimization over Heterogeneous Information Networks

Social influence analysis has shown great potential for strategic marketing decision. It is well known that people influence one another based on both their social connections and the social activities that they have engaged in the past. In this article, we develop an innovative and high-performance social influence based graph clustering framework with four unique features. First, we explicitly distinguish social connection based influence (self-influence) and social activity based influence (co-influence). We compute the self-influence similarity between two members based on their social connections within a single collaboration network, and compute the co-influence similarity by taking into account not only the set of activities that people participate but also the semantic association between these activities. Second, we define the concept of influence-based similarity by introducing a unified influence-based similarity matrix that employs an iterative weight update method to integrate self-influence and co-influence similarities. Third, we design a dynamic learning algorithm, called SI-C luster , for social influence based graph clustering. It iteratively partitions a large social collaboration network into K clusters based on both the social network itself and the multiple associated activity information networks, each representing a category of activities that people have engaged. To make the SI-C luster algorithm converge fast, we transform sophisticated nonlinear fractional programming problem with respect to multiple weights into a straightforward nonlinear parametric programming problem of single variable. Finally, we develop an optimization technique of diagonalizable-matrix approximation to speed up the computation of self-influence similarity and co-influence similarities. Our SI-Cluster-Opt significantly improves the efficiency of SI-Cluster on large graphs while maintaining high quality of clustering results. Extensive experimental evaluation on three real-world graphs shows that, compared to existing representative graph clustering algorithms, our SI-C luster -O pt approach not only achieves a very good balance between self-influence and co-influence similarities but also scales extremely well for clustering large graphs in terms of time complexity while meeting the guarantee of high density, low entropy and low Davies--Bouldin Index.

Towards understanding longitudinal collaboration networks: a case of mammography performance research

In this paper, we explore the longitudinal research collaboration network of `mammography performance' over 30 years by creating and analysing a large collaboration network data using Scopus. The study of social networks using longitudinal data may provide new insights into how this collaborative research evolve over time as well as what type of actors influence the whole network in time. The methods and findings presented in this work aim to assist identifying key actors in other research collaboration networks. In doing so, we apply a rank aggregation technique to centrality measures in order to derive a single ranking of influential actors. We argue that there is a strong correlation between the level of degree and closeness centralities of an actor and its influence in the research collaboration network (at macro/country level).

Structural Cohesion: Visualization and Heuristics for Fast Computation

Abstract The structural cohesion model is a powerful theoretical conception of cohesion in social groups, but its diffusion in empirical literature has been hampered by operationalization and computational problems. In this paper, we start from the classic definition of structural cohesion as the minimum number of actors who need to be removed in a network in order to disconnect it, and extend it by using average node connectivity as a finer grained measure of cohesion. We present useful heuristics for computing structural cohesion that allow a speed-up of one order of magnitude over the algorithms currently available. We analyze three large collaboration networks (co-maintenance of Debian packages, co-authorship in Nuclear Theory and High-Energy Theory) and show how our approach can help researchers measure structural cohesion in relatively large networks. We also introduce a novel graphical representation of the structural cohesion analysis to quickly spot differences across networks.

Maximizing the Spread of Influence through a Social Network

Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of “word of mouth” in the promotion of new products. Motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target? We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here. The two conference papers upon which this article is based (KDD 2003 and ICALP 2005) provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks. We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform node-selection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks. The present article is an expanded version of two conference papers which appeared in KDD 2003 and ICALP 2005, respectively.

Structure and Overlaps of Ground-Truth Communities in Networks

One of the main organizing principles in real-world networks is that of network communities , where sets of nodes organize into densely linked clusters. Even though detection of such communities is of great interest, understanding the structure communities in large networks remains relatively limited. In particular, due to the unavailability of labeled ground-truth data, it was traditionally very hard to develop accurate models of network community structure. Here we use six large social, collaboration, and information networks where nodes explicitly state their ground-truth community memberships. For example, nodes in social networks join into explicitly defined interest based groups, and we use such groups as explicitly labeled ground-truth communities. We use such ground-truth communities to study their structural signatures by analyzing how ground-truth communities emerge in networks and how they overlap. We observe some surprising phenomena. First, ground-truth communities contain high-degree hub nodes that reside in community overlaps and link to most of the members of the community. Second, the overlaps of communities are more densely connected than the non-overlapping parts of communities. We show that this in contrast to the conventional wisdom that community overlaps are more sparsely connected than the non-overlapping parts themselves. We then show that many existing models of network communities do not capture dense community overlaps. This in turn means that most present models and community detection methods confuse overlaps as separate communities. In contrast, we present the community-affiliation graph model (AGM), a conceptual model of network community structure. We demonstrate that AGM reliably captures the overall structure of networks as well as the overlapping and hierarchical nature of network communities.

Defining and evaluating network communities based on ground-truth

Nodes in real-world networks organize into densely linked communities where edges appear with high concentration among the members of the community. Identifying such communities of nodes has proven to be a challenging task due to a plethora of definitions of network communities, intractability of methods for detecting them, and the issues with evaluation which stem from the lack of a reliable gold-standard ground-truth. In this paper, we distinguish between structural and functional definitions of network communities. Structural definitions of communities are based on connectivity patterns, like the density of connections between the community members, while functional definitions are based on (often unobserved) common function or role of the community members in the network. We argue that the goal of network community detection is to extract functional communities based on the connectivity structure of the nodes in the network. We then identify networks with explicitly labeled functional communities to which we refer as ground-truth communities. In particular, we study a set of 230 large real-world social, collaboration, and information networks where nodes explicitly state their community memberships. For example, in social networks, nodes explicitly join various interest-based social groups. We use such social groups to define a reliable and robust notion of ground-truth communities. We then propose a methodology, which allows us to compare and quantitatively evaluate how different structural definitions of communities correspond to ground-truth functional communities. We study 13 commonly used structural definitions of communities and examine their sensitivity, robustness and performance in identifying the ground-truth. We show that the 13 structural definitions are heavily correlated and naturally group into four classes. We find that two of these definitions, Conductance and Triad participation ratio, consistently give the best performance in identifying ground-truth communities. We also investigate a task of detecting communities given a single seed node. We extend the local spectral clustering algorithm into a heuristic parameter-free community detection method that easily scales to networks with more than 100 million nodes. The proposed method achieves 30 % relative improvement over current local clustering methods.

Coautoría y redes de colaboración científica de la pediatría española (2006-2010)

IntroductionScientific collaboration is very important, as it is the basis of the scientific development of every discipline. The aim of this paper is to identify the indicators of scientific collaboration and co-authorship networks of Spanish researchers and institutions publishing in national and international paediatric, multidisciplinary or other knowledge areas journals during the period 2006-2010. MethodsThe papers studied were obtained from the databases including, Science Citation Index Expanded, Scopus, Índice Médico Español and Índice Bibliográfico Español en Ciencias de la Salud, by means of applying different search profiles. All the papers signed by co-authors were quantified in order to identify the authorship and institutional collaboration networks. Furthermore the degree, betweenness index, and closeness index were obtained as a measurement of the structural analysis. Co-authorships were represented graphically by the network analysis and display software Pajek. ResultsA total of 7971 articles were published during the period 2006-2010, with 90.55% completed in collaboration. Using a threshold of 10 or more co-authorships, 77 research groups in Pediatrics were identified. Most papers were published in collaboration between institutions of the same Autonomous Community (42.28%), and 14.84% with international collaboration. The analysis of institutional participation enabled a large nucleus or institutional collaboration network to be identified, with 52 linked institutions. International collaboration was led by the USA and European countries, such as United Kingdom, Germany and Italy. DiscussionAuthors, institutions and the most active working groups in Spanish pediatrics were identified, which is very interesting information to establish contacts to increase the existing networks, to prevent redundancies, and to take advantage of the new emerging groups. It is necessary to promote the collaboration of Spanish researchers, especially with their international colleagues, since a positive relationship is found between international collaboration and quality and impact of publications measured by citation analysis.

C-index: A weighted network node centrality measure for collaboration competence

This paper proposes a new node centrality measurement index (c-index) and its derivative indexes (iterative c-index and cg-index) to measure the collaboration competence of a node in a weighted network. We prove that c-index observe the power law distribution in the weighted scale-free network. A case study of a very large scientific collaboration network indicates that the indexes proposed in this paper are different from other common centrality measures (degree centrality, betweenness centrality, closeness centrality, eigenvector centrality and node strength) and other h-type indexes (lobby-index, w-lobby index and h-degree). The c-index and its derivative indexes proposed in this paper comprehensively utilize the amount of nodes’ neighbors, link strengths and centrality information of neighbor nodes to measure the centrality of a node, composing a new unique centrality measure for collaborative competency.

Spectral evolution in dynamic networks

We introduce and study the spectral evolution model, which characterizes the growth of large networks in terms of the eigenvalue decomposition of their adjacency matrices: In large networks, changes over time result in a change of a graph’s spectrum, leaving the eigenvectors unchanged. We validate this hypothesis for several large social, collaboration, rating, citation, and communication networks. Following these observations, we introduce two link prediction algorithms based on the learning of the changes to a network’s spectrum. These new link prediction methods generalize several common graph kernels that can be expressed as spectral transformations. The first method is based on reducing the link prediction problem to a one-dimensional curve-fitting problem which can be solved efficiently. The second algorithm extrapolates a network’s spectrum to predict links. Both algorithms are evaluated on fifteen network datasets for which edge creation times are known.

Diversity of social ties in scientific collaboration networks

Diversity is one of the important perspectives to characterize behaviors of individuals in social networks. It is intuitively believed that diversity of social ties accounts for competition advantage and idea innovation. However, quantitative evidences in a real large social network can be rarely found in the previous research. Thanks to the availability of scientific publication records on WWW; now we can construct a large scientific collaboration network, which provides us a chance to gain insight into the diversity of relationships in a real social network through statistical analysis. In this article, we dedicate our efforts to perform empirical analysis on a scientific collaboration network extracted from DBLP, an online bibliographic database in computer science, in a systematical way, finding the following: distributions of diversity indices tend to decay in an exponential or Gaussian way; diversity indices are not trivially correlated to existing vertex importance measures; authors of diverse social ties tend to connect to each other and these authors are generally more competitive than others.

FAST INFORMATION PROPAGATION IN SOCIAL NETWORKS

Social networks are attracting more attention from both industry and academia. Prior works on social networks focus on analyzing their roles in information propagation and decision making. However, few of these works consider the latency of information propagation, which is an essential issue especially in time-critical scenarios in social networks. In this paper, we introduce a new problem called FAST INFORMATION PROPAGATION PROBLEM, which is an optimization problem to identify the minimum set of influential nodes that could influence the whole network within a given latency bound d. We show our complexity result on this problem in the deterministic threshold models and present a greedy hill-climbing algorithm as the solution. For the d = 1 case, we prove that under the majority threshold model, this approximation algorithm has a performance ratio of H(Δ + 1) - 1 + ⌈ Δ/2⌉. Extensive experiments are conducted on a large collaboration network and the results show that our approximation algorithm outperforms the node-selection heuristics, which utilize the well-known approaches based on the node centrality in the social network analysis.

Comparative definition of community and corresponding identifying algorithm

A comparative definition for community in networks is proposed, and the corresponding detecting algorithm is given. A community is defined as a set of nodes, which satisfies the requirement that each node's degree inside the community should not be smaller than the node's degree toward any other community. In the algorithm, the attractive force of a community to a node is defined as the connections between them. Then employing an attractive-force-based self-organizing process, without any extra parameter, the best communities can be detected. Several artificial and real-world networks, including the Zachary karate club, college football, and large scientific collaboration networks, are analyzed. The algorithm works well in detecting communities, and it also gives a nice description of network division and group formation.