scholarly journals Discovering Small Target Sets in Social Networks: A Fast and Effective Algorithm

Algorithmica ◽  
2017 ◽  
Vol 80 (6) ◽  
pp. 1804-1833 ◽  
Author(s):  
Gennaro Cordasco ◽  
Luisa Gargano ◽  
Marco Mecchia ◽  
Adele A. Rescigno ◽  
Ugo Vaccaro
Keyword(s):  
Social Networks ◽  
Small Target ◽  
Effective Algorithm ◽  
Target Sets

A Fast and Effective Heuristic for Discovering Small Target Sets in Social Networks

2015 ◽  
pp. 193-208 ◽  
Author(s):  
Gennaro Cordasco ◽  
Luisa Gargano ◽  
Marco Mecchia ◽  
Adele A. Rescigno ◽  
Ugo Vaccaro
Keyword(s):  
Social Networks ◽  
Small Target ◽  
Target Sets

A cost-effective algorithm for inferring the trust between two individuals in social networks

2019 ◽  
Vol 164 ◽  
pp. 122-138 ◽  
Author(s):  
Chengying Mao ◽  
Changfu Xu ◽  
Qiang He
Keyword(s):  
Social Networks ◽  
Cost Effective ◽  
Effective Algorithm

Influence of Category Size and Target Set Size on Unconscious Priming by Novel Words

2008 ◽  
Vol 55 (3) ◽  
pp. 189-194 ◽  
Author(s):  
Richard L. Abrams
Keyword(s):  
Present Experiment ◽  
Farm Animals ◽  
Small Target ◽  
Category Size ◽  
Small Farm ◽  
Set Size ◽  
Word Priming ◽  
Target Set ◽  
Target Sets ◽  
Novel Words

Abstract. In support of their argument that unconscious priming by novel words is critically influenced by target set size, Kiesel, Kunde, Pohl, and Hoffman (2006) report priming from novel words when target sets were large but not when they were small. The present experiment examined the possibility that target set size interacts with category size. (In both experiments in Kiesel et al., category size was large.) In the present experiment, with a small target set, novel-word priming did occur when categories were small (farm animals, fruits) but not when categories were large (larger or smaller than a computer monitor). This finding suggests that, contrary to the position advanced by Kiesel et al., priming when target sets are small involves a mechanism other than preactivation of perceptual features belonging to the target set.

scholarly journals MASTER: across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation

2018 ◽  
Author(s):  
Sen Su ◽  
Li Sun ◽  
Zhongbao Zhang ◽  
Gen Li ◽  
Jielun Qu
Keyword(s):  
Social Networks ◽  
Real World ◽  
State Of The Art ◽  
The State ◽  
Effective Algorithm ◽  
Kkt Points ◽  
Real World Datasets ◽  
Significant Attention

Recently, reconciling social networks receives significant attention. Most of the existing studies have limitations in the following three aspects: multiplicity, comprehensiveness and robustness. To address these three limitations, we rethink this problem and propose the MASTER framework, i.e., across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation. In this framework, we first design a novel Constrained Dual Embedding model by simultaneously embedding and reconciling multiple social networks to formulate our problem into a unified optimization. To address this optimization, we then design an effective algorithm called NS-Alternating. We also prove that this algorithm converges to KKT points. Through extensive experiments on real-world datasets, we demonstrate that MASTER outperforms the state-of-the-art approaches.

MANAGING UNCERTAINTY IN WEB-BASED SOCIAL NETWORKS

2012 ◽  
Vol 20 (supp01) ◽  
pp. 147-158 ◽  
Author(s):  
SHAOJIE QIAO ◽  
TIANRUI LI ◽  
YAN YANG ◽  
CHRISTOPHER C. YANG
Keyword(s):  
Social Networks ◽  
Binary Relation ◽  
Transition Matrix ◽  
Network Formation ◽  
Transition Probability ◽  
Centrality Measures ◽  
Effective Algorithm ◽  
Web Based ◽  
Criminal Network ◽  
Full Consideration

Identifying key members from web-based social networks assists in assessing the risk of criminal network formation. To manage the uncertainty in complex web-based social networks, we first formally defined the binary relation and uncertainty of pages in web-based social networks. Secondly, we proposed an effective algorithm for Mining Key member from uncertain web-based social networks, called MiKey, by integrating uncertainty of pages into three centrality measures including degree, betweenness, and closeness. MiKey takes into a full consideration of the uncertainty in web-based social networks by computing the transition probability from one page to another. Furthermore, we briefly introduced the approach of calculating the k-order transition matrix of pages. Finally, we conducted experiments on real web data and the results show that MiKey is effective in discovering key pages from web-based social networks with less time deficiency than the centrality measures based algorithm.

Some Recent Results in Quiescent Prominence Spectrometry

1979 ◽  
Vol 44 ◽  
pp. 41-47
Author(s):  
Donald A. Landman
Keyword(s):  
Real Time ◽  
Computer System ◽  
Spectral Response ◽  
Absolute Intensity ◽  
Solar Observatory ◽  
Target Size ◽  
Small Target ◽  
Signal Averaging ◽  
Quiescent Prominence

This paper describes some recent results of our quiescent prominence spectrometry program at the Mees Solar Observatory on Haleakala. The observations were made with the 25 cm coronagraph/coudé spectrograph system using a silicon vidicon detector. This detector consists of 500 contiguous channels covering approximately 6 or 80 Å, depending on the grating used. The instrument is interfaced to the Observatory’s PDP 11/45 computer system, and has the important advantages of wide spectral response, linearity and signal-averaging with real-time display. Its principal drawback is the relatively small target size. For the present work, the aperture was about 3″ × 5″. Absolute intensity calibrations were made by measuring quiet regions near sun center.

Multilayer Social Networks

2016 ◽  
Author(s):  
Mark E. Dickison ◽  
Matteo Magnani ◽  
Luca Rossi
Keyword(s):  
Social Networks ◽  
Multilayer Social Networks

Hostility and Perceptions of Support in Ambiguous Social Interactions

2006 ◽  
Vol 27 (2) ◽  
pp. 108-115 ◽  
Author(s):  
Ana-Maria Vranceanu ◽  
Linda C. Gallo ◽  
Laura M. Bogart
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Information Processing ◽  
Social Interactions ◽  
Social Information Processing ◽  
Perceived Social Support ◽  
Social Information ◽  
Inverse Association ◽  
Processing Bias ◽  
Information Processing Bias

The present study investigated whether a social information processing bias contributes to the inverse association between trait hostility and perceived social support. A sample of 104 undergraduates (50 men) completed a measure of hostility and rated videotaped interactions in which a speaker disclosed a problem while a listener reacted ambiguously. Results showed that hostile persons rated listeners as less friendly and socially supportive across six conversations, although the nature of the hostility effect varied by sex, target rated, and manner in which support was assessed. Hostility and target interactively impacted ratings of support and affiliation only for men. At least in part, a social information processing bias could contribute to hostile persons' perceptions of their social networks.

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