Exploring the low-energy landscape of large-scale signed social networks

2012 ◽  
Vol 86 (3) ◽  
Author(s):  
G. Facchetti ◽  
G. Iacono ◽  
C. Altafini
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Energy Landscape ◽  
Low Energy ◽  
Signed Social Networks

scholarly journals A Community-Based Approach for Link Prediction in Signed Social Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Saeed Reza Shahriary ◽  
Mohsen Shahriari ◽  
Rafidah MD Noor
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Link Prediction ◽  
Large Scale ◽  
Prediction Problem ◽  
Signed Graphs ◽  
Community Based ◽  
Ranking Algorithms ◽  
Detection Approach ◽  
Signed Social Networks

In signed social networks, relationships among nodes are of the types positive (friendship) and negative (hostility). One absorbing issue in signed social networks is predicting sign of edges among people who are members of these networks. Other than edge sign prediction, one can define importance of people or nodes in networks via ranking algorithms. There exist few ranking algorithms for signed graphs; also few studies have shown role of ranking in link prediction problem. Hence, we were motivated to investigate ranking algorithms availed for signed graphs and their effect on sign prediction problem. This paper makes the contribution of using community detection approach for ranking algorithms in signed graphs. Therefore, community detection which is another active area of research in social networks is also investigated in this paper. Community detection algorithms try to find groups of nodes in which they share common properties like similarity. We were able to devise three community-based ranking algorithms which are suitable for signed graphs, and also we evaluated these ranking algorithms via sign prediction problem. These ranking algorithms were tested on three large-scale datasets: Epinions, Slashdot, and Wikipedia. We indicated that, in some cases, these ranking algorithms outperform previous works because their prediction accuracies are better.

scholarly journals Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks

2021 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Christos Makris ◽  
Georgios Pispirigos
Keyword(s):  
Social Networks ◽  
Graph Partitioning ◽  
Large Scale ◽  
Real Life ◽  
Information Networks ◽  
Digital Marketing ◽  
Partitioning Problems ◽  
Iterative Solutions ◽  
Community Extraction ◽  
Stability And Accuracy

Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.

scholarly journals Metastable Intermediates as Stepping Stones on the Maturation Pathways of Viral Capsids

mBio ◽  
2014 ◽  
Vol 5 (6) ◽  
Author(s):  
Giovanni Cardone ◽  
Robert L. Duda ◽  
Naiqian Cheng ◽  
Lili You ◽  
James F. Conway ◽  
...  
Keyword(s):  
Conformational Changes ◽  
Large Scale ◽  
Structural Changes ◽  
Energy Landscape ◽  
Potential Hazard ◽  
Stepping Stones ◽  
Scanning Calorimetry ◽  
Conformational Effects ◽  
Capsid Maturation ◽  
Capsid Integrity

ABSTRACT As they mature, many capsids undergo massive conformational changes that transform their stability, reactivity, and capacity for DNA. In some cases, maturation proceeds via one or more intermediate states. These structures represent local minima in a rich energy landscape that combines contributions from subunit folding, association of subunits into capsomers, and intercapsomer interactions. We have used scanning calorimetry and cryo-electron microscopy to explore the range of capsid conformations accessible to bacteriophage HK97. To separate conformational effects from those associated with covalent cross-linking (a stabilization mechanism of HK97), a cross-link-incompetent mutant was used. The mature capsid Head I undergoes an endothermic phase transition at 60°C in which it shrinks by 7%, primarily through changes in its hexamer conformation. The transition is reversible, with a half-life of ~3 min; however, >50% of reverted capsids are severely distorted or ruptured. This observation implies that such damage is a potential hazard of large-scale structural changes such as those involved in maturation. Assuming that the risk is lower for smaller changes, this suggests a rationalization for the existence of metastable intermediates: that they serve as stepping stones that preserve capsid integrity as it switches between the radically different conformations of its precursor and mature states. IMPORTANCE Large-scale conformational changes are widespread in virus maturation and infection processes. These changes are accompanied by the release of conformational free energy as the virion (or fusogenic glycoprotein) switches from a precursor state to its mature state. Each state corresponds to a local minimum in an energy landscape. The conformational changes in capsid maturation are so radical that the question arises of how maturing capsids avoid being torn apart. Offering proof of principle, severe damage is inflicted when a bacteriophage HK97 capsid reverts from the (nonphysiological) state that it enters when heated past 60°C. We suggest that capsid proteins have been selected in part by the criterion of being able to avoid sustaining collateral damage as they mature. One way of achieving this—as with the HK97 capsid—involves breaking the overall transition down into several smaller steps in which the risk of damage is reduced.

scholarly journals Exploiting Proximity-Based Mobile Apps for Large-Scale Location Privacy Probing

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Shuang Zhao ◽  
Xiapu Luo ◽  
Xiaobo Ma ◽  
Bo Bai ◽  
Yankang Zhao ◽  
...  
Keyword(s):  
Social Networks ◽  
Case Studies ◽  
Real World ◽  
Emission Reduction ◽  
Large Scale ◽  
Location Privacy ◽  
Mobile Apps ◽  
Physical World ◽  
Typical Type ◽  
Location Spoofing

Proximity-based apps have been changing the way people interact with each other in the physical world. To help people extend their social networks, proximity-based nearby-stranger (NS) apps that encourage people to make friends with nearby strangers have gained popularity recently. As another typical type of proximity-based apps, some ridesharing (RS) apps allowing drivers to search nearby passengers and get their ridesharing requests also become popular due to their contribution to economy and emission reduction. In this paper, we concentrate on the location privacy of proximity-based mobile apps. By analyzing the communication mechanism, we find that many apps of this type are vulnerable to large-scale location spoofing attack (LLSA). We accordingly propose three approaches to performing LLSA. To evaluate the threat of LLSA posed to proximity-based mobile apps, we perform real-world case studies against an NS app named Weibo and an RS app called Didi. The results show that our approaches can effectively and automatically collect a huge volume of users’ locations or travel records, thereby demonstrating the severity of LLSA. We apply the LLSA approaches against nine popular proximity-based apps with millions of installations to evaluate the defense strength. We finally suggest possible countermeasures for the proposed attacks.

Exploiting Social Networks for Large-Scale Human Behavior Modeling

2011 ◽  
Vol 10 (4) ◽  
pp. 45-53 ◽  
Author(s):  
Nicholas D. Lane ◽  
Ye Xu ◽  
Hong Lu ◽  
Andrew T. Campbell ◽  
Tanzeem Choudhury ◽  
...  
Keyword(s):  
Social Networks ◽  
Human Behavior ◽  
Large Scale ◽  
Behavior Modeling ◽  
Human Behavior Modeling

scholarly journals Large-scale low-energy excitations in 3-d spin glasses

2000 ◽  
Vol 18 (3) ◽  
pp. 467-477 ◽  
Author(s):  
J. Houdayer ◽  
F. Krzakala ◽  
O.C. Martin
Keyword(s):  
Spin Glasses ◽  
Large Scale ◽  
Low Energy
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