Supervised Random Walks: Predicting and Recommending Links in Social Networks

Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Although this problem has been extensively studied, the challenge of how to effectively combine the information from the network structure with rich node and edge attribute data remains largely open. We develop an algorithm based on Supervised Random Walks that naturally combines the information from the network structure with node and edge level attributes. We achieve this by using these attributes to guide a random walk on the graph. We formulate a supervised learning task where the goal is to learn a function that assigns strengths to edges in the network such that a random walker is more likely to visit the nodes to which new links will be created in the future. We develop an efficient training algorithm to directly learn the edge strength estimation function. Our experiments on the Facebook social graph and large collaboration networks show that our approach outperforms state-of-the-art unsupervised approaches as well as approaches that are based on feature extraction

Comparative Raman Studies of Sr2RuO4, Sr3Ru2O7 and Sr4Ru3O10

The polarized Raman spectra of layered ruthenates of the Srn+1RunO3n+1 (n=1,2,3) Ruddlesden-Popper series were measured between 10 and 300 K. The phonon spectra of Sr3Ru2O7 and Sr4Ru3O10 confirmed earlier reports for correlated rotations of neighboring RuO6 octahedra within double or triple perovskite blocks. The observed Raman lines of Ag or B1g symmetry were assigned to particular atomic vibrations by considering the Raman modes in simplified structures with only one double or triple RuO6 layer per unit cell and by comparison to the predictions of lattice dynamical calculations for the real Pban and Pbam structures. Along with discrete phonon lines, a continuum scattering, presumably of electronic origin, is present in the zz, xx and xy, but not in the x'y' and zx spectra. Its interference with phonons results in Fano shape for some of the lines in the xx and xy spectra. The temperature dependencies of phonon parameters of Sr3Ru2O7 exhibit no anomaly between 10 and 300 K where no magnetic transition occurs. In contrast, two B1g lines in the spectra of Sr4Ru3O10, corresponding to oxygen vibrations modulating the Ru-O-Ru bond angle, show noticeable hardening with ferromagnetic ordering at 105 K, thus indicating strong spin-phonon interaction.Comment: 9 pages, 12 figure

Models and Algorithms for Graph Watermarking

We introduce models and algorithmic foundations for graph watermarking. Our frameworks include security definitions and proofs, as well as characterizations when graph watermarking is algorithmically feasible, in spite of the fact that the general problem is NP-complete by simple reductions from the subgraph isomorphism or graph edit distance problems. In the digital watermarking of many types of files, an implicit step in the recovery of a watermark is the mapping of individual pieces of data, such as image pixels or movie frames, from one object to another. In graphs, this step corresponds to approximately matching vertices of one graph to another based on graph invariants such as vertex degree. Our approach is based on characterizing the feasibility of graph watermarking in terms of keygen, marking, and identification functions defined over graph families with known distributions. We demonstrate the strength of this approach with exemplary watermarking schemes for two random graph models, the classic Erd\H{o}s-R\'{e}nyi model and a random power-law graph model, both of which are used to model real-world networks

Flow graphs: interweaving dynamics and structure

The behavior of complex systems is determined not only by the topological organization of their interconnections but also by the dynamical processes taking place among their constituents. A faithful modeling of the dynamics is essential because different dynamical processes may be affected very differently by network topology. A full characterization of such systems thus requires a formalization that encompasses both aspects simultaneously, rather than relying only on the topological adjacency matrix. To achieve this, we introduce the concept of flow graphs, namely weighted networks where dynamical flows are embedded into the link weights. Flow graphs provide an integrated representation of the structure and dynamics of the system, which can then be analyzed with standard tools from network theory. Conversely, a structural network feature of our choice can also be used as the basis for the construction of a flow graph that will then encompass a dynamics biased by such a feature. We illustrate the ideas by focusing on the mathematical properties of generic linear processes on complex networks that can be represented as biased random walks and also explore their dual consensus dynamics.Comment: 4 pages, 1 figur

Analytical reasoning task reveals limits of social learning in networks

Social learning -by observing and copying others- is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is our ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of lab-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions, and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an 'unreflective copying bias,' which limits their social learning to the output, rather than the process, of their peers' reasoning -even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behavior through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning

An efficient and principled method for detecting communities in networks

A fundamental problem in the analysis of network data is the detection of network communities, groups of densely interconnected nodes, which may be overlapping or disjoint. Here we describe a method for finding overlapping communities based on a principled statistical approach using generative network models. We show how the method can be implemented using a fast, closed-form expectation-maximization algorithm that allows us to analyze networks of millions of nodes in reasonable running times. We test the method both on real-world networks and on synthetic benchmarks and find that it gives results competitive with previous methods. We also show that the same approach can be used to extract nonoverlapping community divisions via a relaxation method, and demonstrate that the algorithm is competitively fast and accurate for the nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl

Mesoscopic structure and social aspects of human mobility

The individual movements of large numbers of people are important in many contexts, from urban planning to disease spreading. Datasets that capture human mobility are now available and many interesting features have been discovered, including the ultra-slow spatial growth of individual mobility. However, the detailed substructures and spatiotemporal flows of mobility - the sets and sequences of visited locations - have not been well studied. We show that individual mobility is dominated by small groups of frequently visited, dynamically close locations, forming primary "habitats" capturing typical daily activity, along with subsidiary habitats representing additional travel. These habitats do not correspond to typical contexts such as home or work. The temporal evolution of mobility within habitats, which constitutes most motion, is universal across habitats and exhibits scaling patterns both distinct from all previous observations and unpredicted by current models. The delay to enter subsidiary habitats is a primary factor in the spatiotemporal growth of human travel. Interestingly, habitats correlate with non-mobility dynamics such as communication activity, implying that habitats may influence processes such as information spreading and revealing new connections between human mobility and social networks.Comment: 7 pages, 5 figures (main text); 11 pages, 9 figures, 1 table (supporting information

Detections of rare species lead citizen scientists to initiate data recording

Funding: Louis Backstrom and Rachel Drake received PhD scholarship funding from CREEM, the Centre for Research into Ecological and Environmental Modelling.Aim:  Citizen science data are increasingly used to monitor biodiversity but come with several challenges that can impair accurate ecological conclusions. We explore how observers' preferences for certain species over others bias the initiation of survey efforts and assess the extent of this sampling bias in semi-structured citizen science datasets. We investigate the effects of this bias on occupancy model-based species distribution models and offer suggestions for mitigating this when analysing citizen science data. Location:  Great Britain, with methods applicable to citizen science datasets worldwide. Methods:  We assess observer species preferences in list initiation via two methods: (1) indirectly through exploration of the relationship between species rarity and survey duration and (2) directly through analysis of the first-recorded species on surveys. We use these results to assess the impact of list-initiation sampling bias on occupancy models of 132 common breeding birds across Britain. Results: We find evidence for list-initiation sampling bias in British eBird and BirdTrack data. This bias is driven by observer preferences for certain species over others, with species preferences correlated with species rarity. This bias was stronger in short-duration surveys, and removing short surveys to remove these biased lists had limited impacts on occupancy models. Conclusions:  Citizen science schemes projects that allow observers freedom in how they observe and record biodiversity consequently have more heterogeneous datasets. Observer species preferences lead to biased initiation of surveys and consequent overreporting of some species. Although we find limited effects of this bias on occupancy models in our study, we nevertheless suggest analysts consider its possible effects when exploring and analysing citizen science data.Peer reviewe

Incidence, management, and outcomes of cardiovascular insufficiency in critically ill term and late preterm newborn infants

OBJECTIVE: The objective of this study was to characterize the incidence, management, and short-term outcomes of cardiovascular insufficiency (CVI) in mechanically ventilated newborns, evaluating four separate prespecified definitions. STUDY DESIGN: Multicenter, prospective cohort study of infants ≥34 weeks gestational age (GA) and on mechanical ventilation during the first 72 hours. CVI was prospectively defined as either (1) mean arterial pressure (MAP) < GA; (2) MAP < GA + signs of inadequate perfusion; (3) any therapy for CVI; or (4) inotropic therapy. Short-term outcomes included death, days on ventilation, oxygen, and to full feedings and discharge. RESULTS: Of 647 who met inclusion criteria, 419 (65%) met ≥1 definition of CVI. Of these, 98% received fluid boluses, 36% inotropes, and 17% corticosteroids. Of treated infants, 46% did not have CVI as defined by a MAP < GA ± signs of inadequate perfusion. Inotropic therapy was associated with increased mortality (11.1 vs. 1.3%; p < 0.05). CONCLUSION: More than half of the infants met at least one definition of CVI. However, almost half of the treated infants met none of the definitions. Inotropic therapy was associated with increased mortality. These findings can help guide the design of future studies of CVI in newborn