Interactive Proofs for Social Graphs

2020 ◽  
pp. 574-601
Author(s):  
Liran Katzir ◽  
Clara Shikhelman ◽  
Eylon Yogev
Keyword(s):  
Interactive Proofs ◽  
Social Graphs

Influence maximization in social graphs based on community structure and node coverage gain

2021 ◽  
Vol 118 ◽  
pp. 327-338
Author(s):  
Zhixiao Wang ◽  
Chengcheng Sun ◽  
Jingke Xi ◽  
Xiaocui Li
Keyword(s):  
Community Structure ◽  
Influence Maximization ◽  
Social Graphs

scholarly journals An Exponential Separation Between MA and AM Proofs of Proximity

2021 ◽  
Vol 30 (2) ◽  
Author(s):  
Tom Gur ◽  
Yang P. Liu ◽  
Ron D. Rothblum
Keyword(s):  
Quantum Information ◽  
Lower Bound ◽  
Recent Result ◽  
Query Complexity ◽  
Random String ◽  
Natural Property ◽  
Public And Private ◽  
Alternate Proof ◽  
Interactive Proofs ◽  
Exponential Separation

AbstractInteractive proofs of proximity allow a sublinear-time verifier to check that a given input is close to the language, using a small amount of communication with a powerful (but untrusted) prover. In this work, we consider two natural minimally interactive variants of such proofs systems, in which the prover only sends a single message, referred to as the proof. The first variant, known as -proofs of Proximity (), is fully non-interactive, meaning that the proof is a function of the input only. The second variant, known as -proofs of Proximity (), allows the proof to additionally depend on the verifier's (entire) random string. The complexity of both s and s is the total number of bits that the verifier observes—namely, the sum of the proof length and query complexity. Our main result is an exponential separation between the power of s and s. Specifically, we exhibit an explicit and natural property $$\Pi$$ Π that admits an with complexity $$O(\log n)$$ O ( log n ) , whereas any for $$\Pi$$ Π has complexity $$\tilde{\Omega}(n^{1/4})$$ Ω ~ ( n 1 / 4 ) , where n denotes the length of the input in bits. Our lower bound also yields an alternate proof, which is more general and arguably much simpler, for a recent result of Fischer et al. (ITCS, 2014). Also, Aaronson (Quantum Information & Computation 2012) has shown a $$\Omega(n^{1/6})$$ Ω ( n 1 / 6 ) lower bound for the same property $$\Pi$$ Π .Lastly, we also consider the notion of oblivious proofs of proximity, in which the verifier's queries are oblivious to the proof. In this setting, we show that s can only be quadratically stronger than s. As an application of this result, we show an exponential separation between the power of public and private coin for oblivious interactive proofs of proximity.

scholarly journals Assessing the Effectiveness of Social and Political Innovations in the Development of Interaction between the Authorities and the Population during COVID-19: The Implication of Open Innovation

2021 ◽  
Vol 7 (3) ◽  
pp. 172
Author(s):  
Elena Kranzeeva ◽  
Evgeny Golovatsky ◽  
Anna Orlova ◽  
Natalia Nyatina ◽  
Anna Burmakina
Keyword(s):  
Open Innovation ◽  
Remote Access ◽  
Border Region ◽  
Regional Innovation ◽  
Data Format ◽  
Digital Platforms ◽  
Innovation Activities ◽  
Political Interaction ◽  
Social Graphs ◽  
Regions Of Russia

Open innovations combine the interaction of the authorities and the population in regions of Russia. Social and political interaction of Russian network users demonstrates new open forms of political participation, mobilization practices (initiative appeals, petitions), the use of expert systems data, and remote access technologies. The increasing number of initiatives and the growth of online communities involved in the discussion and adjustment of the results of innovation activities require the use of a big data format. The demand for open innovation based on the principles of transparency of social and political interactions is being updated during COVID-19. This study aims to assess the effectiveness of open innovations in social and political interactions during COVID-19. The innovative practices of communication between the population and authorities were studied using DataMining tools based on digital platforms: “Russian Public Initiative”, “Change.org” and “GoogleTrends”. Users’ social graphs represent the visualization in terms of thematic and territorial groupings. The results obtained allow for a conclusion about the dependence of the regional innovation activities on the openness of their communications and their location relative to authoritative and other types of resources. The physical location of the region (center–border region–periphery) and dependence on implementation at the federal, regional or municipal levels are circumstances influencing the effectiveness of social and political innovations.

Constant-Round Interactive Proofs for Delegating Computation

2019 ◽  
pp. STOC16-255-STOC16-340
Author(s):  
Omer Reingold ◽  
Guy N. Rothblum ◽  
Ron D. Rothblum
Keyword(s):  
Interactive Proofs

scholarly journals A UNIFIED COMMUNITY DETECTION, VISUALIZATION AND ANALYSIS METHOD

2014 ◽  
Vol 17 (01) ◽  
pp. 1450001 ◽  
Author(s):  
MICHEL CRAMPES ◽  
MICHEL PLANTIÉ
Keyword(s):  
Community Detection ◽  
Directed Graphs ◽  
Real Data ◽  
Semantic Interpretation ◽  
Detection Methods ◽  
Analysis Method ◽  
Overlapping Communities ◽  
Social Graphs ◽  
Tractography Data ◽  
Research Domain

With the widespread social networks on the Internet, community detection in social graphs has recently become an important research domain. Interest was initially limited to unipartite graph inputs and partitioned community outputs. More recently, bipartite graphs, directed graphs and overlapping communities have all been investigated. Few contributions however have encompassed all three types of graphs simultaneously. In this paper, we present a method that unifies community detection for these three types of graphs while at the same time it merges partitioned and overlapping communities. Moreover, the results are visualized in a way that allows for analysis and semantic interpretation. For validation purposes this method is experimented on some well-known simple benchmarks and then applied to real data: photos and tags in Facebook and Human Brain Tractography data. This last application leads to the possibility of applying community detection methods to other fields such as data analysis with original enhanced performances.

Know Thy Neighbor: Towards Optimal Mapping of Contacts to Social Graphs for DTN Routing

2010 ◽  
Author(s):  
Theus Hossmann ◽  
Thrasyvoulos Spyropoulos ◽  
Franck Legendre
Keyword(s):  
Dtn Routing ◽  
Social Graphs
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