Program

Complex networks are robust to random failures; but not always to targeted attacks. The resilience of complex networks towards different node targeted attacks are studied immensely in the literature. Many node attack strategies were also proposed, and their efficiency was compared. However, in each of these proposals, the scientists used different measures of efficiency. So, it doesn't seem easy to compare them and choose the one most suitable for the system under examination. Here, we review the main results from the literature on centrality based node attack strategies. Our focus is only on the works on undirected and unweighted networks. We want to highlight the necessity of a more realistic measure of attack efficiency.

In many fields, networks are well-established tools for modelling complex systems. In neuroscience, brain activity is often summarized as a functional connectivity network, which examines the correlations between pairs of interacting brain regions. While powerful, the network model is limited by the restriction that only pairwise dependencies are visible and potentially higher-order structures are missed. In this talk, I will explore how multivariate information theory can reveal higher-order, synergistic dependencies in the human brain. We calculate the O-information, a measure of whether the structure of a system is redundancy- or synergy-dominated, and show that synergistic subsystems are widespread in the human brain. Highly synergistic subsystems typically sit between canonical functional networks and may serve to integrate those networks. We then use simulated annealing to find maximally synergistic subsystems, finding that such systems typically comprise ~10 brain regions, also recruited from multiple canonical brain systems. Though ubiquitous, highly synergistic subsystems are invisible when considering traditional, pairwise functional connectivity, and form a structure that has been unrecognized by established network-based analyses. Higher-order interactions in the brain represent a vast and under-explored space that, made accessible with tools of multivariate information theory, may offer novel scientific insights. The O-information is a widely applicable framework, and analyses like the one presented here of other systems may reveal synergistic structures formerly undisclosed by bivariate network analyses.

A key unexamined area of the citation gap and bias in visibility including the online dissemination of scholars’ work is how the intersectional relationship between co-author teams’ gender and ethnic diversity potentially influences the link between online visibility and citation impact. To address this gap in the literature, we compile a comprehensive data set that includes 14 different broad research areas to examine longitudinally (over 7 years) the relationship between articles’ online visibility and their citations for teams of varying gender and ethnic diversity. Though the average diversity of co-author teams has not changed significantly between 2013 and 2019, our findings suggest that teams with higher gender or ethnic diversity are more likely to produce articles among the top 25% most cited, but there is an intersectional penalty for teams with high gender and ethnic diversity. Even taking online visibility into account, intersectional diversity predicts lower success. Our work points to the need to recognize the disadvantages diverse research groups face and to promote them with specific funds, calls, and opportunities since the scientific community is less likely to cite them regardless of the online visibility they receive.

Networks of coupled phase-oscillators are often used to describe and analyze a broad array of phenomena running from heartbeats to flashing fireflies, to the high voltage electric grid. In many of these phenomena, it is desirable for the oscillators participating in the network to not only achieve but maintain a particular type of behavior called frequency synchronization. To this end, we propose a novel mathematical framework for designing robust networks of coupled phase oscillators by leveraging a vulnerability measure that quantifies how much a small perturbation to a phase oscillator's natural frequency impacts the system's global synchronized frequencies. Given a fixed complex network topology with specific governing dynamics, the proposed framework finds an optimal allocation of edge weights that minimizes such vulnerability measure(s) at the node(s) for which we expect perturbations to occur by solving a tractable semi-definite programming problem.

COVID outbreak emerges as an external shock that alters typical mobility configuration. Commencing from outside the system, its catastrophic impact may perpetuate individual mobility that is already constrained by socioeconomic stratification. We aim to study the variability of how an adjustment in mobility is made by individuals and whether the response level to deal with the changing condition is dictated by their socioeconomic status. Mobility data is retrieved from the CUEBIQ dataset, covering temporal transitions during pandemics namely Before Lockdown (BL), Lockdown (L), and Reopening (R). Bogota retains the longest temporal observation until May 2021 with 55K people/25 million trajectories, followed by London (February 2021) comprising 200K people/115 million trajectories, Jakarta (December 2020) constituting 65K/26 million trajectories, and New York (July 2020) with 277K/30 million trajectories. We rely on income data from the Central Bureau of Statistics at census tract (or unit of comparable size) to determine the socio-economic status of both people (based on home inference) and places. We refer to the stringency index on Oxford Covid-19 Government Response Tracker (OxCGRT) dataset to identify the responsiveness of behavioural change by SES classes. These results show that the first lockdown induced a considerable increase in mobility segregation, but the attempt to loosening mobility restriction (BL-R) did not necessarily diminish isolation within the own neighbourhood (as previously induced by lockdown), indicating that recovery is not fully made.

Some online fame is a function of offline fame (e.g. Barack Obama’s Twitter account), but other accounts come to prominence more organically. They then have attentional capital that they can use for personal gain or lift up causes important to them. Jorts the cat, originally the subject of a viral Reddit Am I The Asshole post, has become the nominal author of a humorous Twitter account that often posts pro-labor content. Jorts was never a famous celebrity offline; his fame is strictly from social media. We use Jorts’ Twitter as a case study in triad closure mechanisms, using the Twitter API to track following events over time; we note that Jorts’ amplification behavior disrupts existing patterns of triad closure and seek to understand how this behavior fits into the existing theory on triad closure in dynamic social networks.

There is a well-documented gap in the observed number of scientific works produced by women and men in science, with clear consequences for the retention and promotion of women in science1. The gap might be a result of productivity differences2-5, or it might be due to women’s contributions not being acknowledged6,7. This paper finds that at least part of this gap is due to the latter: women in research teams are significantly less likely to be credited with authorship than are men. The findings are consistent across three very different sources of data. Analysis of the first source - large scale administrative data on research teams, team scientific output, and attribution of credit - show that women are significantly less likely to be named on any given article or patent produced by their team relative to their peers. The gender gap in attribution is found across almost all scientific fields and career stages. The second source – an extensive survey of authors – similarly shows that women’s scientific contributions are systematically less likely to be recognized. The third source – qualitative responses – suggests that the reason is that their work is often not known, not appreciated, or ignored. At least some of the observed gender gap in scientific output may not be due to differences in scientific contribution, but to differences in attribution.

To be announced soon !