Manipulating concept spread using concept relationships

The propagation of concepts in a population of agents is a form of influence spread, which can be modelled as a cascade from a set of initially activated individuals. The study of such influence cascades, in particular the identification of influential individuals, has a wide range of applications including epidemic control, viral marketing and the study of social norms. In real-world environments there may be many concepts spreading and interacting. These interactions can affect the spread of a given concept, either boosting it and allowing it to spread further, or inhibiting it and limiting its capability to spread. Previous work does not consider how the interactions between concepts affect concept spread. Taking concept interactions into consideration allows for indirect concept manipulation, meaning that we can affect concepts we are not able to directly control. In this paper, we consider the problem of indirect concept manipulation, and propose heuristics for indirectly boosting or inhibiting concept spread in environments where concepts interact. We define a framework that allows for the interactions between any number of concepts to be represented, and present a heuristic that aims to identify important influence paths for a given target concept in order to manipulate its spread. We compare the performance of this heuristic, called maximum probable gain, against established heuristics for manipulating influence spread

Feeling happy enhances early spatial encoding of peripheral information automatically: electrophysiological time-course and neural sources.

Previous research has shown that positive mood may broaden attention, although it remains unclear whether this effect has a perceptual or a postperceptual locus. In this study, we addressed this question using high-density event-related potential methods. We randomly assigned participants to a positive or a neutral mood condition. Then they performed a demanding oddball task at fixation (primary task ensuring fixation) and a localization task of peripheral stimuli shown at three positions in the upper visual field (secondary task) concurrently. While positive mood did not influence behavioral performance for the primary task, it did facilitate stimulus localization on the secondary task. At the electrophysiological level, we found that the amplitude of the C1 component (reflecting an early retinotopic encoding of the stimulus in V1) was enhanced in the positive, as compared with the neutral, mood group. Importantly, this effect appeared to be largely automatic, because it occurred regardless of the task relevance of the peripheral stimulus and prior to top-down gain control effects seen at the level of the subsequent P1 component. This early effect was also observed irrespective of a change of the target-related P300 component (primary task) by positive mood. These results suggest that positive mood can automatically boost the spatial encoding of peripheral stimuli early on following stimulus onset. This effect can eventually underlie the broadening of spatial attention, which has been associated with this specific mood state

Protection against Contagion in Complex Networks

In real-world complex networks, harmful spreads, commonly known as contagions, are common and can potentially lead to catastrophic events if uncontrolled. Some examples include pandemics, network attacks on crucial infrastructure systems, and the propagation of misinformation or radical ideas. Thus, it is critical to study the protective measures that inhibit or eliminate contagion in these networks. This is known as the network protection problem. The network protection problem investigates the most efficient graph manipulations (e.g., node and/or edge removal or addition) to protect a certain set of nodes known as critical nodes. There are two types of critical nodes: (1) predefined, based on their importance to the functionality of the network; (2) unknown, whose importance depends on their location in the network structure. For both of these groups and with no assumption on the contagion dynamics, I address three major shortcomings in the current network protection research: namely, scalability, imprecise evaluation metric, and assumption on global graph knowledge. First, to address the scalability issue, I show that local community information affects contagion paths through characteristic path length. The relationship between the two suggests that, instead of global network manipulations, we can disrupt the contagion paths by manipulating the local community of critical nodes. Next, I study network protection of predefined critical nodes against targeted contagion attacks with access to partial network information only. I propose the CoVerD protection algorithm that is fast and successfully increases the attacker’s effort for reaching the target nodes by 3 to 10 times compared to the next best-performing benchmark. Finally, I study the more sophisticated problem of protecting unknown critical nodes in the context of biological contagions, with partial and no knowledge of network structure. In the presence of partial network information, I show that strategies based on immediate neighborhood information give the best trade-off between performance and cost. In the presence of no network information, I propose a dynamic algorithm, ComMit, that works within a limited budget and enforces bursts of short-term restriction on small communities instead of long-term isolation of unaffected individuals. In comparison to baselines, ComMit reduces the peak of infection by 73% and shortens the duration of infection by 90%, even for persistent spreads

Strategic distribution of seeds to support diffusion in complex networks

© 2018 Jankowski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Usually, the launch of the diffusion process is triggered by a few early adopters–i.e., seeds of diffusion. Many studies have assumed that all seeds are activated once to initiate the diffusion process in social networks and therefore are focused on finding optimal ways of choosing these nodes according to a limited budget. Despite the advances in identifying influencing spreaders, the strategy of activating all seeds at the beginning might not be sufficient in accelerating and maximising the coverage of diffusion. Also, it does not capture real scenarios in which marketing campaigns continuously monitor and support the diffusion process by seeding more nodes. More recent studies investigate the possibility of activating additional seeds as the diffusion process goes forward. In this work, we further examine this approach and search for optimal ways of distributing seeds during the diffusion process according to a pre-allocated seeding budget. Theoretically, we show that a universally best solution does not exist, and we prove that finding an optimal distribution of supporting seeds over time for a particular network is an NP-hard problem. Numerically, we evaluate several seeding strategies on different networks regarding maximising the coverage and minimising the spreading time. We find that each network topology has a best strategy given some spreading parameters. Our findings can be crucial in identifying the best strategies for budget allocation in different scenarios such as marketing or political campaigns

Strategies For Overcoming the Grand Challenges of Implementing Environmental Flows: A Coupled Human and Natural Systems Perspective

Global declines in freshwater biodiversity and the ecosystem services freshwater ecosystems provide have prompted a call for accelerated and expanded implementation of conservation interventions to bend the curve on these losses. Environmental flows are recognized as a powerful freshwater conservation tool to boost biodiversity and ecosystem services through targeted water releases from dams, however widespread implementation is lacking. Because implementing environmental flows requires careful planning and consideration of both humans and nature, there are many challenges conservation planners face as they attempt to boost implementation. The research presented in this dissertation aims to provide strategies to overcome the challenges facing environmental flows implementation by conceptualizing and modeling environmental flows as a coupled human and natural system. The first project presented a conceptual framework to identify locations with both high biodiversity value and conservation feasibility to target for e-flows implementation across future climate uncertainty. Despite climate uncertainty, some locations were identified as high conservation priority. This research suggests that despite significant conservation planning challenges, environmental flows can still be implemented, and offers a conservation planning framework that can be used in other settings. The second project tested different simulated scenarios in an incentive-based Payment for Ecosystem Services (PES) water conservation initiative to identify tradeoffs between equity and conservation outcomes. This research found that aiming for an equitable distribution of payments to reduce water consumption and reallocate that water to environmental flows does not result in large tradeoffs to conservation outcomes. This research suggests that prioritizing equity does not sacrifice conservation outcomes and provides a framework for testing equity tradeoffs in PES schemes. The third project surveyed water decision-makers to identify their perspectives on the barriers and data needs to implementing environmental flows. This research found that despite decision makers’ different perspectives on future water conditions, they identified the same barriers and data needs. This research suggests that cooperation on complex human-environmental problems could happen despite strongly held values and beliefs that might otherwise inhibit implementation. The fourth project tested whether targeting influential individuals in conservation networks as early adopters of an environmental flows initiative would boost overall adoption. This research found robust results that targeting influential individuals boosted overall adoption across spatial scales and information diffusion models. This research suggests that to help accelerate and expand environmental flows initiative adoption, influential individuals should be targeted as early adopters. The results presented in this dissertation contribute to current high-priority research efforts in conservation science that aim to help bend the curve on freshwater biodiversity loss by accelerating and expanding the implementation of environmental flows. Overall, considering the needs of both people and nature is key to successful environmental flows implementation

The Attentional Boost Effect: What limits and what causes it? A behavioural and functional study in older adults, euthymic bipolar patients and healthy subjects

La tesi rigurda un fenomeno controintuitivo chiamato “Attentional Boost Effect” (“Effetto dell’aumento attentivo”). Questo effetto è caratterizzato dal fatto che la detezione di un target (che richiede una risposta) durante la codifica di uno stimolo ad esso contemporaneo può migliorare la prestazione mnestica per quello stimolo in fase di test, rispetto a stimoli presentati contemporaneamente ad un distrattore (che non richiede una risposta). L’ “Attentional Boost Effect” è stato indagato con differenti tipi di materiali, in differenti condizioni attentive, con differenti paradigmi e in varie popolazioni. La tesi inizia con un’introduzione teorica dove sono esaminati la maggior parte degli studi effettuati sull’ “Attentional Boost Effect”. Sono inoltre presentati due capitoli in cui vengono riassunti i cambiamenti, principalmente cognitivi, che avvengono durante l’invecchiamento normale e nel disturbo bipolare. Infine, viene discusso un breve capitolo speculativo sul possibile ruolo di alcuni neurotrasmettitori nella genesi del fenomeno oggetto di interesse. In questo lavoro di tesi si è voluto estendere le conoscenze sull’ “Attentional Boost Effect”, iniziando con l’indagarne la presenza in anziani sani e pazienti bipolari eutimici, entrambe popolazioni che riportano disturbi cognitivi, principalmente attentivi e mnesici, rispetto a giovani controlli. Sono stati eseguiti 4 esperimenti sugli anziani, cambiando il materiale usato nel compito di memoria, il tipo di istruzioni date ai partecipanti e il tempo di presentazione dello stimolo in fase di codifica. In tutti gli esperimenti, l’effetto è robusto e significativo nel campione di controllo di giovani adulti mentre è abolito nel campione di anziani sani. È stato inoltre eseguito un ulteriore esperimento reclutando pazienti bipolari in una fase di remissione, che non hanno mostrato l’effetto. Interessante, i risultati in quest’ultimo studio sembrano indicare che l’ampiezza dell’effetto tende a diminuire all’aumentare dell’età (oltre i 35 anni) nel campione di controlli sani. Infine, è stato eseguito un esperimento di risonanza magnetica funzionale, volendo indagare l’attivazione cerebrale correlata al fenomeno in un campione di giovani adulti. I risultati indicano che una rete attentiva ventrale sembra essere alla base dell’effetto. Nel complesso, i dati sono in linea con la presenza di un deficit delle funzioni attentive negli anziani sani e nei pazienti bipolari eutimici che sarebbe alla base dell’assenza dell’effetto boost in queste popolazioni. Altre possibili ipotesi di spiegazione sono discusse nel capitolo conclusivo

Identifying Influential Agents In Social Systems

This dissertation addresses the problem of influence maximization in social networks. In- fluence maximization is applicable to many types of real-world problems, including modeling contagion, technology adoption, and viral marketing. Here we examine an advertisement domain in which the overarching goal is to find the influential nodes in a social network, based on the network structure and the interactions, as targets of advertisement. The assumption is that advertisement budget limits prevent us from sending the advertisement to everybody in the network. Therefore, a wise selection of the people can be beneficial in increasing the product adoption. To model these social systems, agent-based modeling, a powerful tool for the study of phenomena that are difficult to observe within the confines of the laboratory, is used. To analyze marketing scenarios, this dissertation proposes a new method for propagating information through a social system and demonstrates how it can be used to develop a product advertisement strategy in a simulated market. We consider the desire of agents toward purchasing an item as a random variable and solve the influence maximization problem in steady state using an optimization method to assign the advertisement of available products to appropriate messenger agents. Our market simulation 1) accounts for the effects of group membership on agent attitudes 2) has a network structure that is similar to realistic human systems 3) models inter-product preference correlations that can be learned from market data. The results on synthetic data show that this method is significantly better than network analysis methods based on centrality measures. The optimized influence maximization (OIM) described above, has some limitations. For instance, it relies on a global estimation of the interaction among agents in the network, rendering it incapable of handling large networks. Although OIM is capable of finding the influential nodes in the social network in an optimized way and targeting them for advertising, in large networks, performing the matrix operations required to find the optimized solution is intractable. To overcome this limitation, we then propose a hierarchical influence maximization (HIM) iii algorithm for scaling influence maximization to larger networks. In the hierarchical method the network is partitioned into multiple smaller networks that can be solved exactly with optimization techniques, assuming a generalized IC model, to identify a candidate set of seed nodes. The candidate nodes are used to create a distance-preserving abstract version of the network that maintains an aggregate influence model between partitions. The budget limitation for the advertising dictates the algorithm’s stopping point. On synthetic datasets, we show that our method comes close to the optimal node selection, at substantially lower runtime costs. We present results from applying the HIM algorithm to real-world datasets collected from social media sites with large numbers of users (Epinions, SlashDot, and WikiVote) and compare it with two benchmarks, PMIA and DegreeDiscount, to examine the scalability and performance. Our experimental results reveal that HIM scales to larger networks but is outperformed by degreebased algorithms in highly-connected networks. However, HIM performs well in modular networks where the communities are clearly separable with small number of cross-community edges. This finding suggests that for practical applications it is useful to account for network properties when selecting an influence maximization method

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task