| 1. |
- Glasbey, JC, et al.
(author)
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- 2021
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swepub:Mat__t
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| 2. |
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| 3. |
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| 4. |
- Davies, Stuart J., et al.
(author)
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ForestGEO: Understanding forest diversity and dynamics through a global observatory network
- 2021
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In: Biological Conservation. - : Elsevier BV. - 0006-3207. ; 253
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Journal article (peer-reviewed)abstract
- ForestGEO is a network of scientists and long-term forest dynamics plots (FDPs) spanning the Earth's major forest types. ForestGEO's mission is to advance understanding of the diversity and dynamics of forests and to strengthen global capacity for forest science research. ForestGEO is unique among forest plot networks in its large-scale plot dimensions, censusing of all stems ≥1 cm in diameter, inclusion of tropical, temperate and boreal forests, and investigation of additional biotic (e.g., arthropods) and abiotic (e.g., soils) drivers, which together provide a holistic view of forest functioning. The 71 FDPs in 27 countries include approximately 7.33 million living trees and about 12,000 species, representing 20% of the world's known tree diversity. With >1300 published papers, ForestGEO researchers have made significant contributions in two fundamental areas: species coexistence and diversity, and ecosystem functioning. Specifically, defining the major biotic and abiotic controls on the distribution and coexistence of species and functional types and on variation in species' demography has led to improved understanding of how the multiple dimensions of forest diversity are structured across space and time and how this diversity relates to the processes controlling the role of forests in the Earth system. Nevertheless, knowledge gaps remain that impede our ability to predict how forest diversity and function will respond to climate change and other stressors. Meeting these global research challenges requires major advances in standardizing taxonomy of tropical species, resolving the main drivers of forest dynamics, and integrating plot-based ground and remote sensing observations to scale up estimates of forest diversity and function, coupled with improved predictive models. However, they cannot be met without greater financial commitment to sustain the long-term research of ForestGEO and other forest plot networks, greatly expanded scientific capacity across the world's forested nations, and increased collaboration and integration among research networks and disciplines addressing forest science.
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| 5. |
- Dhamal, Swapnil Vilas, 1988, et al.
(author)
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A Multi-phase Approach for Improving Information Diffusion in Social Networks
- 2015
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In: AAMAS 2015 - Proceedings of the 14th International Conference on Autonomous Agents and MultiAgent Systems. - 9781450334136 ; , s. 1787-1788
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Conference paper (peer-reviewed)abstract
- For maximizing influence spread in a social network, given a certain budget on the number of seed nodes, we investigate the effects of selecting and activating the seed nodes in multiple phases. In particular, we formulate an appropriate objective function for two-phase influence maximization under the independent cascade model, investigate its properties, and propose algorithms for determining the seed nodes in the two phases. We also study the problem of determining an optimal budget-split and delay between the two phases.
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| 6. |
- Dhamal, Swapnil Vilas, 1988, et al.
(author)
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Formation of Stable Strategic Networks with Desired Topologies
- 2015
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In: Studies in Microeconomics. - : SAGE Publications. - 2321-0222 .- 2321-8398. ; 3:2, s. 158-213
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Journal article (peer-reviewed)abstract
- Many real-world networks, such as social networks, consist of strategic agents. The topology of these networks often plays a crucial role in determining the ease and speed with which certain information-driven tasks can be accomplished. Consequently, growing a stable network of a certain desired topology is of interest. Motivated by this, we study the following important problem: Given a certain desired topology, under what conditions would best response link alteration strategies adopted by strategic agents lead to formation of a stable network having the given topology and no other topology. This problem is the inverse of the classical network formation problem where we are concerned with determining stable topologies, given the conditions on the network parameters. We study this interesting inverse problem by proposing (1) a recursive model of network formation and (2) a utility model that captures key determinants of network formation. Building upon these models, we explore relevant topologies such as star graph complete graph, bipartite Turán graph, and multiple stars with interconnected centres. We derive a set of sufficient conditions under which these topologies uniquely emerge, study their social welfare properties and investigate the effects of deviating from the derived conditions.
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| 7. |
- Dhamal, Swapnil Vilas, 1988, et al.
(author)
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Forming Networks of Strategic Agents with Desired Topologies
- 2012
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In: WINE 2012 - Proceedings of the 8th International Conference on Internet and Network Economics. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 9783642353109 ; , s. 504-511
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Conference paper (peer-reviewed)abstract
- Many networks such as social networks and organizational networks in global companies consist of self-interested agents. The topology of these networks often plays a crucial role in important tasks such as information diffusion and information extraction. Consequently, growing a stable network having a certain topology is of interest. Motivated by this, we study the following important problem: given a certain desired network topology, under what conditions would best response (link addition/deletion) strategies played by self-interested agents lead to formation of a stable network having that topology. We study this interesting reverse engineering problem by proposing a natural model of recursive network formation and a utility model that captures many key features. Based on this model, we analyze relevant network topologies and derive a set of sufficient conditions under which these topologies emerge as pairwise stable networks, wherein no node wants to delete any of its links and no two nodes would want to create a link between them.
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| 8. |
- Dhamal, Swapnil Vilas, 1988, et al.
(author)
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Information Diffusion in Social Networks in Two Phases
- 2016
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In: IEEE Transactions on Network Science and Engineering. - 2327-4697. ; 3:4, s. 197-210
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Journal article (peer-reviewed)abstract
- The problem of maximizing information diffusion, given a certain budget expressed in terms of the number of seed nodes, is an important topic in social networks research. Existing literature focuses on single phase diffusion where all seed nodes are selected at the beginning of diffusion and all the selected nodes are activated simultaneously. This paper undertakes a detailed investigation of the effect of selecting and activating seed nodes in multiple phases. Specifically, we study diffusion in two phases assuming the well-studied independent cascade model. First, we formulate an objective function for two-phase diffusion, investigate its properties, and propose efficient algorithms for finding seed nodes in the two phases. Next, we study two associated problems: (1) budget splitting which seeks to optimally split the total budget between the two phases and (2) scheduling which seeks to determine an optimal delay after which to commence the second phase. Our main conclusions include: (a) under strict temporal constraints, use single phase diffusion, (b) under moderate temporal constraints, use two-phase diffusion with a short delay while allocating most of the budget to the first phase, and (c) when there are no temporal constraints, use two-phase diffusion with a long delay while allocating roughly one-third of the budget to the first phase.
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| 9. |
- Dhamal, Swapnil Vilas, 1988, et al.
(author)
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Modeling Spread of Preferences in Social Networks for Sampling-based Preference Aggregation
- 2019
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In: IEEE Transactions on Network Science and Engineering. - 2327-4697. ; 6:1, s. 46-59
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Journal article (peer-reviewed)abstract
- Given a large population, it is an intensive task to gather individual preferences over a set of alternatives and arrive at an aggregate or collective preference of the population. We show that social network underlying the population can be harnessed to accomplish this task effectively, by sampling preferences of a small subset of representative nodes. We first develop a Facebook app to create a dataset consisting of preferences of nodes and the underlying social network, using which, we develop models that capture how preferences are distributed among nodes in a typical social network. We hence propose an appropriate objective function for the problem of selecting best representative nodes. We devise two algorithms, namely, Greedy-min which provides a performance guarantee for a wide class of popular voting rules, and Greedy-sum which exhibits excellent performance in practice. We compare the performance of these proposed algorithms against random-polling and popular centrality measures, and provide a detailed analysis of the obtained results. Our analysis suggests that selecting representatives using social network information is advantageous for aggregating preferences related to personal topics (e.g., lifestyle), while random polling with a reasonable sample size is good enough for aggregating preferences related to social topics (e.g., government policies).
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| 10. |
- Dhamal, Swapnil Vilas, 1988, et al.
(author)
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Scalable Preference Aggregation in Social Networks
- 2013
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In: HCOMP 2013 - Proceedings of the First AAAI Conference on Human Computation and Crowdsourcing. ; , s. 42-50
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Conference paper (peer-reviewed)abstract
- In social choice theory, preference aggregation refers to computing an aggregate preference over a set of alternatives given individual preferences of all the agents. In real-world scenarios, it may not be feasible to gather preferences from all the agents. Moreover, determining the aggregate preference is computationally intensive. In this paper, we show that the aggregate preference of the agents in a social network can be computed efficiently and with sufficient accuracy using preferences elicited from a small subset of critical nodes in the network. Our methodology uses a model developed based on real-world data obtained using a survey on human subjects, and exploits network structure and homophily of relationships. Our approach guarantees good performance for aggregation rules that satisfy a property which we call expected weak insensitivity. We demonstrate empirically that many practically relevant aggregation rules satisfy this property. We also show that two natural objective functions in this context satisfy certain properties, which makes our methodology attractive for scalable preference aggregation over large scale social networks. We conclude that our approach is superior to random polling while aggregating preferences related to individualistic metrics, whereas random polling is acceptable in the case of social metrics.
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| 11. |
- Ghalme, Ganesh, et al.
(author)
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Ballooning Multi-Armed Bandits
- 2020
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In: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS. - 1548-8403 .- 1558-2914. ; , s. 1849-1851
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Conference paper (peer-reviewed)abstract
- We introduce ballooning multi-armed bandits (BL-MAB), a novel extension to the classical stochastic MAB model. In the BL-MAB model, the set of available arms grows (or balloons) over time. The regret in a BL-MAB setting is computed with respect to the best available arm at each time. We first observe that the existing stochastic MAB algorithms are not regret-optimal for the BL-MAB model. We show that if the best arm is equally likely to arrive at any time, a sub-linear regret cannot be achieved, irrespective of the arrival of other arms. We further show that if the best arm is more likely to arrive in the early rounds, one can achieve sub-linear regret. Making reasonable assumptions on the arrival distribution of the best arm in terms of the thinness of the distribution's tail, we prove that the proposed algorithm achieves sub-linear instance-independent regret. We further quantify explicit dependence of regret on the arrival distribution parameters.
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| 12. |
- Ghalme, Ganesh, et al.
(author)
-
Ballooning multi-armed bandits
- 2021
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In: Artificial Intelligence. - : Elsevier BV. - 0004-3702. ; 296
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Journal article (peer-reviewed)abstract
- In this paper, we introduce ballooning multi-armed bandits (BL-MAB), a novel extension of the classical stochastic MAB model. In the BL-MAB model, the set of available arms grows (or balloons) over time. In contrast to the classical MAB setting where the regret is computed with respect to the best arm overall, the regret in a BL-MAB setting is computed with respect to the best available arm at each time. We first observe that the existing stochastic MAB algorithms result in linear regret for the BL-MAB model. We prove that, if the best arm is equally likely to arrive at any time instant, a sub-linear regret cannot be achieved. Next, we show that if the best arm is more likely to arrive in the early rounds, one can achieve sub-linear regret. Our proposed algorithm determines (1) the fraction of the time horizon for which the newly arriving arms should be explored and (2) the sequence of arm pulls in the exploitation phase from among the explored arms. Making reasonable assumptions on the arrival distribution of the best arm in terms of the thinness of the distribution's tail, we prove that the proposed algorithm achieves sub-linear instance-independent regret. We further quantify explicit dependence of regret on the arrival distribution parameters. We reinforce our theoretical findings with extensive simulation results. We conclude by showing that our algorithm would achieve sub-linear regret even if (a) the distributional parameters are not exactly known, but are obtained using a reasonable learning mechanism or (b) the best arm is not more likely to arrive early, but a large fraction of arms is likely to arrive relatively early.
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| 13. |
- Mondal, Sneha, et al.
(author)
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Two-Phase Influence Maximization in Social Networks with Seed Nodes and Referral Incentives
- 2017
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In: ICWSM 2017 - Proceedings of the 11th International AAAI Conference on Web and Social Media. - 2334-0770 .- 2162-3449. - 9781577357889 ; 11:1, s. 620-623
-
Conference paper (peer-reviewed)abstract
- The problem of maximizing the spread of influence with a limited budget is central to social networks research. Most solution approaches available in the existing literature devote the entire budget towards triggering diffusion at seed nodes. This paper investigates the effect of splitting the budget across two different, sequential phases. In phase 1, we adopt the classical approach of initiating diffusion at a selected seed-set. In phase 2, we use the remaining budget to offer referral incentives. We formulate this problem and explore suitable ways to split the budget between the two phases, with detailed experiments on synthetic and real-world datasets. The principal findings from our study are: (a) when the budget is low, it is prudent to use the entire budget for phase 1; (b) when the budget is moderate to high, it is preferable to use much of the budget for phase 1, while allocating the remaining budget to phase 2; (c) in the presence of moderate to strict temporal constraints, phase 2 is not warranted; (d) if the temporal constraints are low or absent, phase 2 yields a decisive improvement in influence spread.
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