Pay few, influence most: Online myopic network covering

Avrachenkov, Konstantin; Basu, Prithwish; Neglia, Giovanni; Ribeiro, Bruno; Towsley, D.

doi:10.1109/infcomw.2014.6849335

Cited by 29 publications

(35 citation statements)

References 43 publications

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“…-Maximum observed degree (MOD). This greedy method proposed in [3] is the current state-of-the-art algorithm for finding the network cover in an online manner.…”

Section: Methodsmentioning

confidence: 99%

Exploring Partially Observed Networks with Nonparametric Bandits

Madhawa

Murata

2018

Studies in Computational Intelligence

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Real-world networks such as social and communication networks are too large to be observed entirely. Such networks are often partially observed such that network size, network topology, and nodes of the original network are unknown. In this paper we formalize the Adaptive Graph Exploring problem. We assume that we are given an incomplete snapshot of a large network and additional nodes can be discovered by querying nodes in the currently observed network. The goal of this problem is to maximize the number of observed nodes within a given query budget. Querying which set of nodes maximizes the size of the observed network? We formulate this problem as an explorationexploitation problem and propose a novel nonparametric multi-arm bandit (MAB) algorithm for identifying which nodes to be queried. Our contributions include: (1) iKNN-UCB, a novel nonparametric MAB algorithm, applies k-nearest neighbor UCB to the setting when the arms are presented in a vector space. (2) provide theoretical guarantee that iKNN-UCB algorithm has sublinear regret and (3) applying iKNN-UCB algorithm on synthetic networks and real-world networks from different domains, we show that our method discovers up to 40% more nodes compared to existing baselines.

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“…-Maximum observed degree (MOD). This greedy method proposed in [3] is the current state-of-the-art algorithm for finding the network cover in an online manner.…”

Section: Methodsmentioning

confidence: 99%

Exploring Partially Observed Networks with Nonparametric Bandits

Madhawa

Murata

2018

Studies in Computational Intelligence

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“…We adapt four representative methods of the above to selective harvesting: Active Sampling [31] (PNB -in reference to the authors surnames), Maximum Observed Degree (MOD) [5], Social Network UCB1 (SN-UCB1) [9], and Active Search (AS) [40]. Table 3 summarizes the key differences between these methods and the proposed method, D 3 TS.…”

Section: Existing Methodsmentioning

confidence: 99%

“…In our simulations we adapt MOD to select the border node with the maximum number of target neighbors in the queried set (ties are resolved randomly). From the expected excess degree results in [5] such border nodes are rich with target neighbors provided that the underlying network exhibits strong homophily with respect to node labels.…”

Section: Existing Methodsmentioning

confidence: 99%

“…Based on Table 2, we pick M={MOD, Active Search, Support Vector Regression, Random Forest, ListNet}. 5 We use this set of classifiers throughout the rest of this paper, unless otherwise noted. One might expect RR's performance to be the average of the performance results yielded by the standalone counterparts, but this is not the case.…”

Section: Tunnel Vision and The Power Of Classifier Diversitymentioning

confidence: 99%

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Selective harvesting over networks

Murai

Rennó

Ribeiro

et al. 2017

Data Min Knowl Disc

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Active search on graphs focuses on collecting certain labeled nodes (targets) given global knowledge of the network topology and its edge weights (encoding pairwise similarities) under a query budget constraint. However, in most current networks, nodes, network topology, network size, and edge weights are all initially unknown. In this work we introduce selective harvesting, a variant of active search where the next node to be queried must be chosen among the neighbors of the current queried node set; the available training data for deciding which node to query is restricted to the subgraph induced by the queried set (and their node attributes) and their neighbors (without any node or edge attributes). Therefore, selective harvesting is a sequential decision problem, where we must decide which node to query at each step. A classifier trained in this scenario can suffer from what we call a tunnel vision effect: without any recourse to independent sampling, the urge to only query promising nodes forces classifiers to gather increasingly biased training data, which we show significantly hurts the performance of active search methods and standard classifiers. We demonstrate that it is possible to collect a much larger set of targets by using multiple classifiers, not by combining their predictions as a weighted ensemble, but switching between classifiers used at each step, as a way to ease the tunnel vision effect. We discover that switching classifiers collects more targets by (a) diversifying the training data and (b) broadening the choices of nodes that can be queried in the future. This highlights an exploration, exploitation, and diversification trade-off in our problem that goes beyond the exploration and exploitation duality found in classic sequential decision problems. Based on these observations we propose D 3 TS, a method based on multi-armed bandits for non-stationary stochastic processes that enforces classifier diversity, which outperforms all competing methods on five real network datasets in our evaluation and exhibits comparable performance on the other two.

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“…We notice that in [1] the knowledge level that they consider is 2) since in their case, they do not have any information about the network topology and they are discovering the network while they are recruiting over the network.…”

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confidence: 99%

Maximum coverage and maximum connected covering in social networks with partial topology information

Reyes

Silva²

2014

2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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Viral marketing campaigns seek to recruit the most influential individuals to cover the largest target audience. This can be modeled as the well-studied maximum coverage problem. There is a related problem when the recruited nodes are connected. It is called the maximum connected cover problem. This problem ensures a strong coordination between the influential nodes which are the backbone of the marketing campaign. In this work, we are interested on both of these problems. Most of the related literature assumes knowledge about the topology of the network. Even in that case, the problem is known to be NP-hard. In this work, we propose heuristics to the maximum connected cover problem and the maximum coverage problem with different knowledge levels about the topology of the network. We quantify the difference between these heuristics and the local and global greedy algorithms.

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Pay few, influence most: Online myopic network covering

Cited by 29 publications

References 43 publications

Exploring Partially Observed Networks with Nonparametric Bandits

Exploring Partially Observed Networks with Nonparametric Bandits

Selective harvesting over networks

Maximum coverage and maximum connected covering in social networks with partial topology information

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