scholarly journals History Sensitive Cascade Model

2011 ◽  
Vol 3 (2) ◽  
pp. 53-66
Author(s):  
Yu Zhang ◽  
Maksim Tsikhanovich ◽  
Georgi Smilyanov
Keyword(s):  
Social Networks ◽  
Lower Bound ◽  
Approximation Algorithms ◽  
Approximation Algorithm ◽  
Real World ◽  
Cascade Model ◽  
Submodular Functions ◽  
Information Cascade ◽  
The Greedy Algorithm ◽  
Over Time

Diffusion is a process by which information, viruses, ideas, or new behavior spread over social networks. Traditional diffusion models are history insensitive, i.e. only giving activated nodes a one-time chance to activate each of its neighboring nodes with some probability. But history dependent interactions between people are often observed in the real world. This paper proposes the History Sensitive Cascade Model (HSCM), a model of information cascade through a network over time. The authors consider the “activation” problem of finding the probability of that a particular node receives information given that some nodes are initially informed. In this paper it is also proven that selecting a set of k nodes with greatest expected influence is NP-hard, and results from submodular functions are used to provide a greedy approximation algorithm with a 1–1/e–e lower bound, where e depends polynomially on the precision of the solution to the “activation” problem. Finally, experiments are performed comparing the greedy algorithm to three other approximation algorithms.

scholarly journals History Sensitive Cascade Model

2012 ◽  
pp. 114-128
Author(s):  
Yu Zhang ◽  
Maksim Tsikhanovich ◽  
Georgi Smilyanov
Keyword(s):  
Social Networks ◽  
Lower Bound ◽  
Approximation Algorithms ◽  
Approximation Algorithm ◽  
Real World ◽  
Cascade Model ◽  
Submodular Functions ◽  
Information Cascade ◽  
The Greedy Algorithm ◽  
Over Time

Diffusion is a process by which information, viruses, ideas, or new behavior spread over social networks. Traditional diffusion models are history insensitive, i.e. only giving activated nodes a one-time chance to activate each of its neighboring nodes with some probability. But history dependent interactions between people are often observed in the real world. This paper proposes the History Sensitive Cascade Model (HSCM), a model of information cascade through a network over time. The authors consider the “activation” problem of finding the probability of that a particular node receives information given that some nodes are initially informed. In this paper it is also proven that selecting a set of k nodes with greatest expected influence is NP-hard, and results from submodular functions are used to provide a greedy approximation algorithm with a 1–1/e–e lower bound, where e depends polynomially on the precision of the solution to the “activation” problem. Finally, experiments are performed comparing the greedy algorithm to three other approximation algorithms.

scholarly journals A Generative Model for Dynamic Networks with Applications

2019 ◽  
Vol 33 ◽  
pp. 7842-7849
Author(s):  
Shubham Gupta ◽  
Gaurav Sharma ◽  
Ambedkar Dukkipati
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Real World ◽  
Link Prediction ◽  
Temporal Dynamics ◽  
Dynamic Networks ◽  
Collaboration Networks ◽  
Proposed Model ◽  
Latent Space ◽  
Over Time

Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches.

scholarly journals Regression under Human Assistance

2020 ◽  
Vol 34 (03) ◽  
pp. 2611-2620
Author(s):  
Abir De ◽  
Paramita Koley ◽  
Niloy Ganguly ◽  
Manuel Gomez-Rodriguez
Keyword(s):  
Machine Learning ◽  
Greedy Algorithm ◽  
Real World ◽  
Medical Diagnosis ◽  
Submodular Functions ◽  
Learning Models ◽  
Real World Data ◽  
Alternative Representation ◽  
The Greedy Algorithm ◽  
Machine Learning Models

Decisions are increasingly taken by both humans and machine learning models. However, machine learning models are currently trained for full automation—they are not aware that some of the decisions may still be taken by humans. In this paper, we take a first step towards the development of machine learning models that are optimized to operate under different automation levels. More specifically, we first introduce the problem of ridge regression under human assistance and show that it is NP-hard. Then, we derive an alternative representation of the corresponding objective function as a difference of nondecreasing submodular functions. Building on this representation, we further show that the objective is nondecreasing and satisfies α-submodularity, a recently introduced notion of approximate submodularity. These properties allow a simple and efficient greedy algorithm to enjoy approximation guarantees at solving the problem. Experiments on synthetic and real-world data from two important applications—medical diagnosis and content moderation—demonstrate that the greedy algorithm beats several competitive baselines.

A novel information cascade model in online social networks

2016 ◽  
Vol 444 ◽  
pp. 297-310 ◽  
Author(s):  
Chao Tong ◽  
Wenbo He ◽  
Jianwei Niu ◽  
Zhongyu Xie
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Cascade Model ◽  
Information Cascade

Systems theories

2018 ◽  
Author(s):  
Marc J. Stern
Keyword(s):  
Social Networks ◽  
Problem Solving ◽  
Natural Resources ◽  
Real World ◽  
Common Pool Resources ◽  
Ecological Systems ◽  
Effective Governance ◽  
Social Ecological Systems ◽  
Social Ecological ◽  
Real World Problem

This chapter covers systems theories relevant to understanding and working to enhance the resilience of social-ecological systems. Social-ecological systems contain natural resources, users of those resources, and the interactions between each. The theories in the chapter share lessons about how to build effective governance structures for common pool resources, how to facilitate the spread of worthwhile ideas across social networks, and how to promote collaboration for greater collective impacts than any one organization alone could achieve. Each theory is summarized succinctly and followed by guidance on how to apply it to real world problem solving.

scholarly journals Real World Performance Evaluation of Transcatheter Aortic Valve Implantation

2021 ◽  
Vol 10 (9) ◽  
pp. 1890
Author(s):  
Gabriele Pesarini ◽  
Gabriele Venturi ◽  
Domenico Tavella ◽  
Leonardo Gottin ◽  
Mattia Lunardi ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Real World ◽  
Baseline Risk ◽  
Risk Scores ◽  
World Population ◽  
Control Analysis ◽  
Transcatheter Aortic Valve ◽  
Procedural Control ◽  
Risk Patients ◽  
Over Time

Background: The aim of this research is to describe the performance over time of transcatheter aortic valve implantations (TAVIs) in a high-volume center with a contemporary, real-world population. Methods: Patients referred for TAVIs at the University Hospital of Verona were prospectively enrolled. By cumulative sum failures analysis (CUSUM), procedural-control curves for standardized combined endpoints—as defined by the Valve Academic Research Consortium-2 (VARC-2)—were calculated and analyzed over time. Acceptable and unacceptable limits were derived from recent studies on TAVI in intermediate and low-risk patients to fit the higher required standards for current indications. Results: A total of 910 patients were included. Baseline risk scores significantly reduced over time. Complete procedural control was obtained after approximately 125 and 190 cases for device success and early safety standardized combined endpoints, respectively. High risk patients (STS ≥ 8) had poorer outcomes, especially in terms of VARC-2 clinical efficacy, and required a higher case load to maintain in-control and proficient procedures. Clinically relevant single endpoints were all influenced by operator’s experience as well. Conclusions: Quality-control analysis for contemporary TAVI interventions based on standardized endpoints suggests the need for relevant operator’s experience to achieve and maintain optimal clinical results, especially in higher-risk subjects.

scholarly journals A Lower Bound for the Query Phase of Contraction Hierarchies and Hub Labels and a Provably Optimal Instance-Based Schema

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 164
Author(s):  
Tobias Rupp ◽  
Stefan Funke
Keyword(s):  
Lower Bound ◽  
Lower Bounds ◽  
Real World ◽  
Road Network ◽  
Upper And Lower Bounds ◽  
Bounded Degree ◽  
Shortest Path Routing ◽  
Graph Classes ◽  
Speed Up ◽  
To Come

We prove a Ω(n) lower bound on the query time for contraction hierarchies (CH) as well as hub labels, two popular speed-up techniques for shortest path routing. Our construction is based on a graph family not too far from subgraphs that occur in real-world road networks, in particular, it is planar and has a bounded degree. Additionally, we borrow ideas from our lower bound proof to come up with instance-based lower bounds for concrete road network instances of moderate size, reaching up to 96% of an upper bound given by a constructed CH. For a variant of our instance-based schema applied to some special graph classes, we can even show matching upper and lower bounds.

scholarly journals POS0619 MODELLING OF DISEASE ACTIVITY IN PATIENTS WITH INFLAMMATORY ARTHROPATHIES TREATED WITH ETANERCEPT ORIGINATOR OR BIOSIMILAR AS FIRST-LINE BIOLOGIC IN AN AUSTRALIAN REAL-WORLD DATASET

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 547.1-547
Author(s):  
C. Deakin ◽  
G. Littlejohn ◽  
H. Griffiths ◽  
T. Smith ◽  
C. Osullivan ◽  
...  
Keyword(s):  
Disease Activity ◽  
Clinical Data ◽  
Real World ◽  
Rank Test ◽  
P Value ◽  
Continuous Variables ◽  
Linear Quadratic ◽  
First Line ◽  
Modest Improvement ◽  
Over Time

Background:The availability of biosimilars as non-proprietary versions of established biologic disease-modifying anti-rheumatic drugs (bDMARDs) is enabling greater access for patients with rheumatic diseases to effective medications at a lower cost. Since April 2017 both the originator and a biosimilar for etanercept (trade names Enbrel and Brenzys, respectively) have been available for use in Australia.Objectives:[1]To model effectiveness of etanercept originator or biosimilar in reducing Disease Activity Score 28-joint count C reactive protein (DAS28CRP) in patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA) or ankylosing spondylitis (AS) treated with either drug as first-line bDMARD[2]To describe persistence on etanercept originator or biosimilar as first-line bDMARD in patients with RA, PsA or ASMethods:Clinical data were obtained from the Optimising Patient outcomes in Australian rheumatoLogy (OPAL) dataset, derived from electronic medical records. Eligible patients with RA, PsA or AS who initiated etanercept originator (n=856) or biosimilar (n=477) as first-line bDMARD between 1 April 2017 and 31 December 2020 were identified. Propensity score matching was performed to select patients on originator (n=230) or biosimilar (n=136) with similar characteristics in terms of diagnosis, disease duration, joint count, age, sex and concomitant medications. Data on clinical outcomes were recorded at 3 months after baseline, and then at 6-monthly intervals. Outcomes data that were missing at a recorded visit were imputed.Effectiveness of the originator, relative to the biosimilar, for reducing DAS28CRP over time was modelled in the matched population using linear mixed models with both random intercepts and slopes to allow for individual heterogeneity, and weighting of individuals by inverse probability of treatment weights to ensure comparability between treatment groups. Time was modelled as a combination of linear, quadratic and cubic continuous variables.Persistence on the originator or biosimilar was analysed using survival analysis (log-rank test).Results:Reduction in DAS28CRP was associated with both time and etanercept originator treatment (Table 1). The conditional R-squared for the model was 0.31. The average predicted DAS28CRP at baseline, 3 months, 6 months, 9 months and 12 months were 4.0 and 4.4, 3.1 and 3.4, 2.6 and 2.8, 2.3 and 2.6, and 2.2 and 2.4 for the originator and biosimilar, respectively, indicating a clinically meaningful effect of time for patients on either drug and an additional modest improvement for patients on the originator.Median time to 50% of patients stopping treatment was 25.5 months for the originator and 24.1 months for the biosimilar (p=0.53). An adverse event was the reason for discontinuing treatment in 33 patients (14.5%) on the originator and 18 patients (12.9%) on the biosimilar.Conclusion:Analysis using a large national real-world dataset showed treatment with either the etanercept originator or the biosimilar was associated with a reduction in DAS28CRP over time, with the originator being associated with a further modest reduction in DAS28CRP that was not clinically significant. Persistence on treatment was not different between the two drugs.Table 1.Respondent characteristics.Fixed EffectEstimate95% Confidence Intervalp-valueTime (linear)0.900.89, 0.911.5e-63Time (quadratic)1.011.00, 1.011.3e-33Time (cubic)1.001.00, 1.007.1e-23Originator0.910.86, 0.960.0013Acknowledgements:The authors acknowledge the members of OPAL Rheumatology Ltd and their patients for providing clinical data for this study, and Software4Specialists Pty Ltd for providing the Audit4 platform.Supported in part by a research grant from Investigator-Initiated Studies Program of Merck & Co Inc, Kenilworth, NJ, USA. The opinions expressed in this paper are those of the authors and do not necessarily represent those of Merck & Co Inc, Kenilworth, NJ, USA.Disclosure of Interests:Claire Deakin: None declared, Geoff Littlejohn Consultant of: Over the last 5 years Geoffrey Littlejohn has received educational grants and consulting fees from AbbVie, Bristol Myers Squibb, Eli Lilly, Gilead, Novartis, Pfizer, Janssen, Sandoz, Sanofi and Seqirus., Hedley Griffiths Consultant of: AbbVie, Gilead, Novartis and Lilly., Tegan Smith: None declared, Catherine OSullivan: None declared, Paul Bird Speakers bureau: Eli Lilly, abbvie, pfizer, BMS, UCB, Gilead, Novartis

scholarly journals An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

2021 ◽  
Vol 5 (1) ◽  
pp. 5
Author(s):  
Ninghan Chen ◽  
Zhiqiang Zhong ◽  
Jun Pang
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Online Social Networks ◽  
Exploratory Study ◽  
Representation Learning ◽  
Data Driven ◽  
Learning Methods ◽  
Twitter Users ◽  
Over Time

The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries.

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.

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