Recent research has highlighted limitations of studying complex systems with time-varying topologies from the perspective of static, time-aggregated networks. Non-Markovian characteristics resulting from the ordering of interactions in temporal networks were identified as one important mechanism that alters causality and affects dynamical processes. So far, an analytical explanation for this phenomenon and for the significant variations observed across different systems is missing. Here we introduce a methodology that allows to analytically predict causality-driven changes of diffusion speed in non-Markovian temporal networks. Validating our predictions in six data sets we show that compared with the time-aggregated network, non-Markovian characteristics can lead to both a slow-down or speed-up of diffusion, which can even outweigh the decelerating effect of community structures in the static topology. Thus, non-Markovian properties of temporal networks constitute an important additional dimension of complexity in time-varying complex systems.

Standfirst Rich data is revealing that complex dependencies between the nodes of a network may escape models based on pairwise interactions. Higher-order network models go beyond these limitations, offering new perspectives for understanding complex systems.

We study correlations in temporal networks and introduce the notion of betweenness preference. It allows to quantify to what extent paths, existing in time-aggregated representations of temporal networks, are actually realizable based on the sequence of interactions. We show that betweenness preference is present in empirical temporal network data and that it influences the length of shortest time-respecting paths. Using four different data sets, we further argue that neglecting betweenness preference leads to wrong conclusions about dynamical processes on temporal networks.Recent works have argued that properties of dynamical processes evolving on complex networks change profoundly when the dynamics of the network topology is taken into account. For a number of empirical temporal networks obtained from time-stamped contact data, simulations have shown that their topological dynamics can both slow down [5,1,6] or speed up spreading processes [17]. At the same time, it has been observed that, compared to time-aggregated topologies, the exploration dynamics of random walks in temporal networks is significantly slower [18]. Furthermore, it has been shown that network dynamics alone can give rise to collective phenomena like synchronization [19]. These observations have generated significant interest in the mechanism underlying these phenomena. A series of recent works focused on the influence of inter-event time distributions and temporal correlations in the time series of interactions [4,6,18,15,9]. Bursty activity patterns of nodes have been identified as one possible source that slows down spreading [6] and random walk processes [16]. Similarly, bursty node activities have been suggested to slow down information diffusion, particularly when the diffusion process is initiated in phases of low activity [13]. Furthermore, for a number of social contact networks, it has been shown that heterogeneous inter-event times increase the length of time-respecting paths [12]. Apart from inter-event time distributions, it has been argued that link appearance frequencies and their correlation with community structures are another characteristic of temporal networks that can slow down spreading dynamics [6]. Another line of research is concerned with the study of temporal motifs [8,20], i.e. whether there are classes of frequently occurring temporal contact patterns. It was shown that the presence of certain temporal motifs (like e.g. "chains" of consec-

We address the question to what extent the success of scientific articles is due to social influence. Analyzing a data set of over 100,000 publications from the field of Computer Science, we study how centrality in the coauthorship network differs between authors who have highly cited papers and those who do not. We further show that a Machine Learning classifier, based only on coauthorship network centrality metrics measured at the time of publication, is able to predict with high precision whether an article will be highly cited five years after publication. By this we provide quantitative insight into the social dimension of scientific publishingchallenging the perception of citations as an objective, socially unbiased measure of scientific success.

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Recent research on temporal networks has highlighted the limitations of a static network perspective for our understanding of complex systems with dynamic topologies. In particular, recent works have shown that i) the specific order in which links occur in real-world temporal networks affects causality structures and thus the evolution of dynamical processes, and ii) higher-order aggregate representations of temporal networks can be used to analytically study the effect of these order correlations on dynamical processes. In this article we analyze the effect of order correlations on path-based centrality measures in real-world temporal networks. Analyzing temporal equivalents of betweenness, closeness and reach centrality in six empirical temporal networks, we first show that an analysis of the commonly used static, time-aggregated representation can give misleading results about the actual importance of nodes. We further study higher-order time-aggregated networks, a recently proposed generalization of the commonly applied static, time-aggregated representation of temporal networks. Here, we particularly define path-based centrality measures based on second-order aggregate networks, empirically validating that node centralities calculated in this way better capture the true temporal centralities of nodes than node centralities calculated based on the commonly used static (first-order) representation. Apart from providing a simple and practical method for the approximation of path-based centralities in temporal networks, our results highlight interesting perspectives for the use of higher-order aggregate networks in the analysis of time-stamped network data.

Efficient bug triaging procedures are an important precondition for successful collaborative software engineering projects. Triaging bugs can become a laborious task particularly in open source software (OSS) projects with a large base of comparably inexperienced parttime contributors. In this paper, we propose an efficient and practical method to identify valid bug reports which a) refer to an actual software bug, b) are not duplicates and c) contain enough information to be processed right away. Our classification is based on nine measures to quantify the social embeddedness of bug reporters in the collaboration network. We demonstrate its applicability in a case study, using a comprehensive data set of more than 700, 000 bug reports obtained from the Bugzilla installation of four major OSS communities, for a period of more than ten years. For those projects that exhibit the lowest fraction of valid bug reports, we find that the bug reporters' position in the collaboration network is a strong indicator for the quality of bug reports. Based on this finding, we develop an automated classification scheme that can easily be integrated into bug tracking platforms and analyze its performance in the considered OSS communities. A support vector machine (SVM) to identify valid bug reports based on the nine measures yields a precision of up to 90.3% with an associated recall of 38.9%. With this, we significantly improve the results obtained in previous case studies for an automated early identification of bugs that are eventually fixed. Furthermore, our study highlights the potential of using quantitative measures of social organization in collaborative software engineering. It also opens a broad perspective for the integration of social awareness in the design of support infrastructures. Software Engineering in Practice (SEIP) Track.bug reports that have been resolved by the community of the Mozilla Firefox project, more than 50, 000 (or ≈ 78%) of these reports have eventually been identified either as duplicates of known bugs, invalid reports that refer to a user error rather than a software issue or incomplete reports which lack basic information required to reproduce the alleged bug. The magnitude of this problem in large-scale projects calls for (semi-)automated techniques that assist bug handling communities in the triaging and prioritization of bug reports. The provision of methods which are able to automatically identify valid bug reports with high precision can have huge implications for practitioners of distributed software engineering: Being able to filter, assign and prioritize those bug reports that likely result in a bug fix can significantly improve the responsiveness of support communities. Furthermore, a temporary deferral of those bug reports that are likely to be duplicates, invalid or incomplete to a moderation queue can considerably alleviate the effort required for bug triaging. It can also be used to automatically enforce the adherence to community guidelines, e.g. by automatically asking original ...

Social organization and division of labor crucially influence the performance of collaborative software engineering efforts. In this paper, we provide a quantitative analysis of the relation between social organization and performance in Gentoo, an Open Source community developing a Linux distribution. We study the structure and dynamics of collaborations as recorded in the project's bug tracking system over a period of ten years. We identify a period of increasing centralization after which most interactions in the community were mediated by a single central contributor. In this period of maximum centralization, the central contributor unexpectedly left the project, thus posing a significant challenge for the community. We quantify how the rise, the activity as well as the subsequent sudden dropout of this central contributor affected both the social organization and the bug handling performance of the Gentoo community. We analyze social organization from the perspective of network theory and augment our quantitative findings by interviews with prominent members of the Gentoo community which shared their personal insights.