Gains, losses and thresholds of influence in social networks

Hamill, J. Todd; Deckro, Richard F.; Wiley, Victor; Renfro, Robert S.

doi:10.1504/ijor.2007.014168

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…Within social networks, people exchange information to ultimately influence others, where influence is defined as an action “to induce a change in the behavior of another that is in accordance with the wishes of the influencer” 13 , 14 . Each individual communicates with (or influences) many other peers and, similarly, individuals are influenced by numerous other peers.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Protecting infrastructure performance from disinformation attacks

Jamalzadeh

Barker

González

et al. 2022

Sci Rep

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Disinformation campaigns are prevalent, affecting vaccination coverage, creating uncertainty in election results, and causing supply chain disruptions, among others. Unfortunately, the problems of misinformation and disinformation are exacerbated due to the wide availability of online platforms and social networks. Naturally, these emerging disinformation networks could lead users to engage with critical infrastructure systems in harmful ways, leading to broader adverse impacts. One such example involves the spread of false pricing information, which causes drastic and sudden changes in user commodity consumption behavior, leading to shortages. Given this, it is critical to address the following related questions: (i) How can we monitor the evolution of disinformation dissemination and its projected impacts on commodity consumption? (ii) What effects do the mitigation efforts of human intermediaries have on the performance of the infrastructure network subject to disinformation campaigns? (iii) How can we manage infrastructure network operations and counter disinformation in concert to avoid shortages and satisfy user demands? To answer these questions, we develop a hybrid approach that integrates an epidemiological model of disinformation spread (based on a susceptible-infectious-recovered model, or SIR) with an efficient mixed-integer programming optimization model for infrastructure network performance. The goal of the optimization model is to determine the best protection and response actions against disinformation to minimize the general shortage of commodities at different nodes over time. The proposed model is illustrated with a case study involving a subset of the western US interconnection grid located in Los Angeles County in California.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Protecting infrastructure performance from disinformation attacks

Jamalzadeh

Barker

González

et al. 2022

Sci Rep

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“…Each of the metrics described above represents a means to quantify the complexity of the network of designers in the context of leadership and communication functions and modes. Each metric provides insight into the ability of leadership or communication to enable the flow of influence and information respectively within the design team [44], [65], [66].…”

Section: Graph Complexity Comparisonmentioning

confidence: 99%

“…Research is also recommended to determine if the degree (leadership) and frequency of influence involved in leadership are similar in the perceptions of student designers. The frequency of communications or leadership functions is also a probability of influence or information passed through these network relations [66].…”

Section: Summary and Future Workmentioning

confidence: 99%

Leadership and Communication Network Identification and Analysis with Dependency Structure Matrices in Senior Design Teams

Righter,

Summers

2021 ASEE Virtual Annual Conference Content Access Proceedings

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A case study is presented that explores informal leadership emergence within capstone student design teams. The study focused on a ten-person, multi-university, multi-disciplinary, twosemester design project focused on unmanned aircraft solution design and build. This study uses a sociometric survey instrument to determine perceived leadership and communication relationships between team members. These relationships are modeled through dependency structure matrices (DSM) that are then analyzed against various complexity and similarity metrics. The network analysis illustrates that relationship leadership networks are less dense than the transitional and action leadership networks. Moreover, communication networks were aligned more with the transition or action leadership networks at corresponding thresholds. This illustrates that not all aspects of leadership can be inferred from the communication processes within a design team.

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