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COC3800
COLLOQUIUM
PRESENTATION
COLLECTIVE MINDS: HOW SOCIAL NETWORK
TOPOLOGY SHAPES COLLECTIVE COGNITION ?
Members
Prabhat Singh 20COB153
Khan Shah Ahmed Shakir Abuasim 20COB206
TITLE
INTRODUCTION
Collective cognition refers to the ways in which individuals in
a group process information and make decisions together[3,4].
Social network topology refers to the pattern of relationships
and connections between individuals in a social network[5].
Different types of social network topology[9]: -
1) Fully connected network
2) Centralized network
3) Decentralized network
4) Scale-free network
RELATED WORK
What has been done ?
• Theoretical Studies
• Empirical Studies
• Recent Advances
What we did ?
Segmented the work into four themes,
• Social network analysis and its applications
• Collective cognition and knowledge construction
• Social influence and behaviour
• Organizational culture and belief systems
THEMES
1.Social network analysis and its applications
Social network analysis (SNA) is a powerful tool for studying the structure
and dynamics of social relationships[11].
Several papers in this theme explore the applications of SNA in different
contexts.
• In sociology, SNA has been used to study social stratification, social movements, and social
capital[11].
• In psychology, SNA has been used to study social support networks and the impact of social
relationships on mental health[13].
• In computer science, SNA has been used to study online communities, social media platforms,
and the spread of viral content[8].
THEMES
2. Collective cognition and knowledge construction
This theme focuses on the processes of knowledge construction and decision-
making in groups, and how these processes are influenced by factors such as
social networks, cognitive structures, and cultural beliefs.
• Studies have shown that groups with high levels of network diversity tend to perform better
on creative tasks, as diverse perspectives and knowledge bases can lead to more innovative
solutions[5].
• Researchers have also explored how cultural beliefs about hierarchy and power impact the
emergence of leadership roles in groups[15].
• Studies have shown that groups with a shared mental model tend to perform better on
complex tasks, as members can more easily coordinate their efforts and anticipate each
other's actions[17].
THEMES
3. Social influence and behaviour
This theme includes papers that explore how social networks and human social
motives can be integrated to achieve social influence at scale, as well as the
neural mechanisms that track popularity in real-world social networks.
• [23] discusses how the popularity of individuals in a social network can be tracked using functional
magnetic resonance imaging (fMRI) and highlights the importance of studying social influence from a
neuroscience perspective.
• [28] suggests that stewardship can be achieved through the development of common understanding,
shared norms, and a sense of common purpose.
• [17] explores how social media can trigger collective efficacy through deliberation.
THEMES
4. Organizational culture and belief systems
The papers under this theme explore the relationship between social
structure, economic outcomes, and entrepreneurial team formation.
• [12] suggest that social networks are not only useful for transmitting information, but they also
play a critical role in building social relationships and creating trust.
• [33] finds that individuals with a high degree of social capital tend to earn more than those with
lower social capital.
• [14] finds that individuals are more likely to form teams with others who have similar network
characteristics, suggesting that social networks play a significant role in shaping
entrepreneurial team formation.
ISSUES AND
CHALLENGES
There are several issues and challenges associated with studying the
relationship between social network topology and collective cognition. Here
are a few:
1) Complexity
2) Data availability
3) Subjectivity
4) Causality
5) Ethical concerns
FUTURE WORK
There are several directions that future work in the study of collective
cognition and social network topology could take. Here are a few:
1) Expanding the scope of analysis
2) Incorporating more diverse populations
3) Examining the impact of interventions
4) Integrating qualitative and quantitative methods
5) Developing new metrics and tools
PUBLICATION TIMELINE
0
5
10
15
20
25
30
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
Documents
Year
Documents by Year
The timeline graph shows that research on this topic has been steadily increasing
since 2008. This suggests that "Collective Minds" is a growing and active area of
research, with increasing interest and attention from researchers.
CONCLUSION
In this literature review, we have explored the ways in which social
network topology influences collective cognition and discussed the
implications of this research for future work.
• One key finding that emerged from this review is that certain network structures, such
as small-world networks and scale-free networks, tend to facilitate the emergence of
collective cognitionmore than others[22].
• It would be valuable to investigate how different network structures and tie strengths
affect the emergence of collective cognitionin different contexts, such as online
communities or organizational settings[18,20].
Overall, this literature review highlights the importance of social network topology in
shaping collective cognitionand provides a foundation for future research aimed at
understanding and harnessing the power of collective cognitionin a variety of contexts[36-
40].
REFERENCE
S
[1] Wang, X., Gan, T., Wei, Y., Wu, J., Meng, D., Nie, L. “Micro-video Tagging via Jointly Modeling Social Influence and Tag Relation” (2022), MM 2022 - Proceedings
of the 30th ACM International Conference on Multimedia, pp. 4478-4486.
[2] Wu, L., Wang, H., Chen, E., Li, Z., Zhao, H., Ma, J. “Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction” (2022), SIGIR 2022 -
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2704-2708.
[3] Xu, X., Deng, K., Zhang, X. “Identifying cost-effective debunkers for multi-stage fake news mitigation campaigns” (2022), WSDM 2022 - Proceedings of the 15th
ACM International Conference on Web Search and Data Mining, pp. 1206-1214.
[4] Jin, B., Guo, W. “Data Driven Modeling Social Media Influence using Differential Equations”,Proceedings of the 2022 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining, ASONAM 2022, pp. 504-507.
[5] Arhachoui, N., Bautista, E., Danisch, M., Giovanidis, A. “A Fast Algorithm for Ranking Users by their Influence in Online Social Platforms” (2022), Proceedings of
the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022, pp. 526-533.
[6] Sartori, C.C., Blum, C. “Boosting a Genetic Algorithm with Graph Neural Networks for Multi-Hop Influence Maximization in Social Networks” (2022),
Proceedings of the 17th Conference on Computer Science and Intelligence Systems, FedCSIS 2022, pp. 363-371.
[7] Cheng, S., Wang, Z., Qian, M., Yang, S., Zheng, X. “MUI-ISIDA: An improved calculation method for user influence in social networks” (2022), Proceedings of
SPIE - The International Society for Optical Engineering, 12260, art. no. 1226027.
[8] Riquelme, F., Muñoz, F., Olivares, R. “Social influence under improved multi-objective metaheuristics” (2021), Proceedings of the 2021 IEEE/ACM International
Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021, pp. 479-486.
REFERENCE
S
[9] Xia, W., Li, Y.,Wu, J., Li, S. “DeepIS: Susceptibility Estimation on Social Networks” (2021), WSDM 2021 - Proceedings of the 14th ACM International
Conference on Web Search and Data Mining, pp.761-769.
[10] Chen, T., Wong, R.C.-W.“An Efficient and Effective Framework for Session-basedSocial Recommendation” (2021), WSDM 2021 - Proceedings of
the 14th ACM International Conference on Web Search and Data Mining, pp. 400-408.
[11] Bo, H., McConville,R.,Hong, J., Liu, W. “Social Influence Prediction with Train and Test Time Augmentation for Graph NeuralNetworks” (2021),
Proceedings of the International Joint Conference on Neural Networks, 2021-July.
[12] Wang, R., Huang, Z.,Liu, S.,Shao, H., Liu, D., Li, J., Wang, T., Sun, D., Yao, S., Abdelzaher,T. “DyDiff-VAE: A Dynamic Variational Framework for
Information Diffusion Prediction” (2021), SIGIR 2021 -Proceedings of the 44th International ACM SIGIR Conference on Research and Development in
Information Retrieval, art. no. 3462934,pp. 163-172.
[13] Konotopska,K., Iacca, G. “Graph-aware evolutionary algorithms for influence maximization” (2021),GECCO2021 Companion - Proceedings of the
2021 Genetic and Evolutionary Computation Conference Companion, pp. 1467-1475.
[14] Poquet,O. “Why birds of a feather flocktogether: Factors triaging students in online forums”(2021),ACM International Conference Proceeding
Series,pp. 469-474.
[15] Saxena, A., Kumar, B.,Gupta, A., Sahasrabudhe, N., Moharir, S. “Influencing Opinions of HeterogeneousPopulations over Finite Time Horizons”
(2021), 2021 International Conference on COMmunication Systems and NETworkS,COMSNETS 2021, art. no. 9352905,pp. 474-482.
[16] Wu, H., Wang, S., Fang, H. “LP-UIT: A Multimodal Framework for Link Prediction in Social Networks”(2021),Proceedings - 2021 IEEE 20th
International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021, pp. 742-749.
REFERENCE
S
[17] Raza, M.R.,Hussain, W., Merigo,J.M. “Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU” (2021), Proceedings - 2021 Innovations in
Intelligent Systems and Applications Conference,ASYU 2021.
[18] Pereira, F.D.C.,De Rezende,P.J.,De Souza, C.C. “Effective Heuristics for the Perfect Awareness Problem”(2021), Procedia Computer Science, 195,
pp. 489-498.
[19] Ko, J., Lee, K., Shin, K., Park, N. “MONSTOR:An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks” (2020),
Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM2020, art. no. 9381460,pp.
204-211.
[20] Khan, T., Michalas, A. “Trust and believe - Should we? evaluating the trustworthiness of twitter users” (2020), Proceedings - 2020 IEEE 19th
International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020, art. no. 9342970,pp. 1791-1800.
[21] Yury,A., Andrade, E., Nogueira,M., Santos, A., Matos, F. “Social-based Cooperation of Vehicles for Data Dissemination of Critical Urban Events”
(2020), 2020 IEEE GlobalCommunications Conference, GLOBECOM2020 - Proceedings,art. no. 9322301,.
[22] Gaitonde, J., Kleinberg,J., Tardos, É. “Adversarial Perturbations of Opinion Dynamics in Networks”(2020),EC 2020 - Proceedings of the 21st ACM
Conference on Economics and Computation, art. no. 3399490,pp. 471-472.
[23] Bo, H., McConville,R., Hong, J., Liu, W. “Social Network Influence Ranking via EmbeddingNetwork Interactions for User Recommendation” (2020),
The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020, pp. 379-384.
[24] Pipergias Analytis, P., Barkoczi,D., Lorenz-Spreen,P., Herzog,S. “The Structure of Social Influence in Recommender Networks” (2020), The Web
Conference 2020 - Proceedingsof the World Wide Web Conference, WWW 2020, pp. 2655-2661.
REFERENCE
S
[25] Oriedi, D., De Runz, C., Guessoum,Z.,Younes,A.A., Nyongesa,H. “Influence maximization through user interaction modelling” (2020),
Proceedings of the ACM Symposium on AppliedComputing, pp. 1888-1890.
[26] Cuzzocrea,A., Leung, C.K.,Deng, D., Mai, J.J., Jiang, F., Fadda, E. “A combined deep-learning and transfer-learning approach for supporting
social influence prediction” (2020), Procedia Computer Science, 177, pp. 170-177.
[27] Trivedi, N.,Singh, A. “Efficient Influence Maximization in Social-Networks under Independent Cascade Model” (2020), Procedia Computer
Science, 173, pp. 315-324.
[28] Bhat, N.,Aggarwal, N.,Kumar, S. “Identification of Influential Spreadersin Social Networks using ImprovedHybrid Rank Method” (2020),
Procedia Computer Science, 171,pp. 662-671.
[29] Aldawish, R., Kurdi, H. “A ModifiedDegree Discount Heuristic for Influence Maximization in Social Networks” (2020), Procedia Computer
Science, 170,pp. 311-316.
[30] Mittal, D., Suthar, P., Patil, M., Pranaya, P.G.S.,Rana, D.P., Tidke, B.“Social Network Influencer Rank Recommender Using DiverseFeatures from
Topical Graph” (2020), Procedia Computer Science, 167,pp. 1861-1871.
[31] Luo, J., Liu, X., Kong, X. “Competitive opinion maximization in social networks” (2019),Proceedingsof the 2019 IEEE/ACM International
Conference on Advances in Social Networks Analysis and Mining, ASONAM2019, pp. 250-257.
[32] Sarkar, S., Shakarian, P., Armenta, M., Sanchez, D.,Lakkaraju, K. “Can social influence be exploited to compromise security: An online
experimental evaluation” (2019),Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining,
ASONAM 2019,pp. 593-596.
REFERENCE
S
[33] Zhang, Y. “Diversifying seedsand audience in social influence maximization” (2019),Proceedings of the 2019 IEEE/ACM International Conference
on Advances in Social Networks Analysis and Mining, ASONAM2019, pp. 89-94.
[34] Sarkar, S., Aleali,A., Shakarian, P., Armenta, M., Sanchez, D., Lakkaraju, K. “Impact of social influence on adoption behavior: An online controlled
experimental evaluation” (2019),Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining,
ASONAM 2019,pp. 226-233.
[35] Wu, L.,Hong, R., Sun, P., Wang, X., Fu, Y.,Wang, M. “A neural influence diffusionmodel for social recommendation” (2019),SIGIR 2019 - Proceedings
of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 235-244.
[36] Eckles,D., Esfandiari, H., Mossel,E., Rahimian, M.A.“Seeding with costly network information”(2019), ACM EC 2019 - Proceedings of the 2019 ACM
Conference on Economics and Computation, pp. 421-422.
[37] Sarkar, S., Alvari, H., Shakarian, P. “Leveraging motifs to modelthe temporal dynamics of diffusionnetworks” (2019),The Web Conference 2019 -
Companion of the World Wide Web Conference, WWW 2019, pp.1079-1086.
[38] Fish, B.,Friedler, S.A.,Bashardoust, A., Scheidegger,C.,Boyd,D., Venkatasubramanian, S. “Gaps in information access in social networks” (2019),
The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, pp.480-490.
[39] Wu, Q., He, P., Zhang, H., Weng, P., Chen, G.,Gao, X., Gao, H. “Dual graph attention networks for deep latent representation of multifaceted social
effects in recommender systems” (2019),The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, pp. 2091-2102.
[40] Zhang, D., Hou, J., Guo,T., Feng, Z.,Pan, H., Liang, Y.,Lin, H., Xia, F. “Judging a bookby its cover: The effect of facial perception on centrality in social
networks” (2019),The Web Conference 2019 -Proceedings of the World Wide Web Conference, WWW 2019, pp. 2290-2300.
THANK
YOU

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Second Presentation.pptx

  • 2. COLLECTIVE MINDS: HOW SOCIAL NETWORK TOPOLOGY SHAPES COLLECTIVE COGNITION ? Members Prabhat Singh 20COB153 Khan Shah Ahmed Shakir Abuasim 20COB206 TITLE
  • 3. INTRODUCTION Collective cognition refers to the ways in which individuals in a group process information and make decisions together[3,4]. Social network topology refers to the pattern of relationships and connections between individuals in a social network[5]. Different types of social network topology[9]: - 1) Fully connected network 2) Centralized network 3) Decentralized network 4) Scale-free network
  • 4. RELATED WORK What has been done ? • Theoretical Studies • Empirical Studies • Recent Advances What we did ? Segmented the work into four themes, • Social network analysis and its applications • Collective cognition and knowledge construction • Social influence and behaviour • Organizational culture and belief systems
  • 5. THEMES 1.Social network analysis and its applications Social network analysis (SNA) is a powerful tool for studying the structure and dynamics of social relationships[11]. Several papers in this theme explore the applications of SNA in different contexts. • In sociology, SNA has been used to study social stratification, social movements, and social capital[11]. • In psychology, SNA has been used to study social support networks and the impact of social relationships on mental health[13]. • In computer science, SNA has been used to study online communities, social media platforms, and the spread of viral content[8].
  • 6. THEMES 2. Collective cognition and knowledge construction This theme focuses on the processes of knowledge construction and decision- making in groups, and how these processes are influenced by factors such as social networks, cognitive structures, and cultural beliefs. • Studies have shown that groups with high levels of network diversity tend to perform better on creative tasks, as diverse perspectives and knowledge bases can lead to more innovative solutions[5]. • Researchers have also explored how cultural beliefs about hierarchy and power impact the emergence of leadership roles in groups[15]. • Studies have shown that groups with a shared mental model tend to perform better on complex tasks, as members can more easily coordinate their efforts and anticipate each other's actions[17].
  • 7. THEMES 3. Social influence and behaviour This theme includes papers that explore how social networks and human social motives can be integrated to achieve social influence at scale, as well as the neural mechanisms that track popularity in real-world social networks. • [23] discusses how the popularity of individuals in a social network can be tracked using functional magnetic resonance imaging (fMRI) and highlights the importance of studying social influence from a neuroscience perspective. • [28] suggests that stewardship can be achieved through the development of common understanding, shared norms, and a sense of common purpose. • [17] explores how social media can trigger collective efficacy through deliberation.
  • 8. THEMES 4. Organizational culture and belief systems The papers under this theme explore the relationship between social structure, economic outcomes, and entrepreneurial team formation. • [12] suggest that social networks are not only useful for transmitting information, but they also play a critical role in building social relationships and creating trust. • [33] finds that individuals with a high degree of social capital tend to earn more than those with lower social capital. • [14] finds that individuals are more likely to form teams with others who have similar network characteristics, suggesting that social networks play a significant role in shaping entrepreneurial team formation.
  • 9. ISSUES AND CHALLENGES There are several issues and challenges associated with studying the relationship between social network topology and collective cognition. Here are a few: 1) Complexity 2) Data availability 3) Subjectivity 4) Causality 5) Ethical concerns
  • 10. FUTURE WORK There are several directions that future work in the study of collective cognition and social network topology could take. Here are a few: 1) Expanding the scope of analysis 2) Incorporating more diverse populations 3) Examining the impact of interventions 4) Integrating qualitative and quantitative methods 5) Developing new metrics and tools
  • 11. PUBLICATION TIMELINE 0 5 10 15 20 25 30 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Documents Year Documents by Year The timeline graph shows that research on this topic has been steadily increasing since 2008. This suggests that "Collective Minds" is a growing and active area of research, with increasing interest and attention from researchers.
  • 12. CONCLUSION In this literature review, we have explored the ways in which social network topology influences collective cognition and discussed the implications of this research for future work. • One key finding that emerged from this review is that certain network structures, such as small-world networks and scale-free networks, tend to facilitate the emergence of collective cognitionmore than others[22]. • It would be valuable to investigate how different network structures and tie strengths affect the emergence of collective cognitionin different contexts, such as online communities or organizational settings[18,20]. Overall, this literature review highlights the importance of social network topology in shaping collective cognitionand provides a foundation for future research aimed at understanding and harnessing the power of collective cognitionin a variety of contexts[36- 40].
  • 13. REFERENCE S [1] Wang, X., Gan, T., Wei, Y., Wu, J., Meng, D., Nie, L. “Micro-video Tagging via Jointly Modeling Social Influence and Tag Relation” (2022), MM 2022 - Proceedings of the 30th ACM International Conference on Multimedia, pp. 4478-4486. [2] Wu, L., Wang, H., Chen, E., Li, Z., Zhao, H., Ma, J. “Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction” (2022), SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2704-2708. [3] Xu, X., Deng, K., Zhang, X. “Identifying cost-effective debunkers for multi-stage fake news mitigation campaigns” (2022), WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining, pp. 1206-1214. [4] Jin, B., Guo, W. “Data Driven Modeling Social Media Influence using Differential Equations”,Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022, pp. 504-507. [5] Arhachoui, N., Bautista, E., Danisch, M., Giovanidis, A. “A Fast Algorithm for Ranking Users by their Influence in Online Social Platforms” (2022), Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022, pp. 526-533. [6] Sartori, C.C., Blum, C. “Boosting a Genetic Algorithm with Graph Neural Networks for Multi-Hop Influence Maximization in Social Networks” (2022), Proceedings of the 17th Conference on Computer Science and Intelligence Systems, FedCSIS 2022, pp. 363-371. [7] Cheng, S., Wang, Z., Qian, M., Yang, S., Zheng, X. “MUI-ISIDA: An improved calculation method for user influence in social networks” (2022), Proceedings of SPIE - The International Society for Optical Engineering, 12260, art. no. 1226027. [8] Riquelme, F., Muñoz, F., Olivares, R. “Social influence under improved multi-objective metaheuristics” (2021), Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021, pp. 479-486.
  • 14. REFERENCE S [9] Xia, W., Li, Y.,Wu, J., Li, S. “DeepIS: Susceptibility Estimation on Social Networks” (2021), WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp.761-769. [10] Chen, T., Wong, R.C.-W.“An Efficient and Effective Framework for Session-basedSocial Recommendation” (2021), WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 400-408. [11] Bo, H., McConville,R.,Hong, J., Liu, W. “Social Influence Prediction with Train and Test Time Augmentation for Graph NeuralNetworks” (2021), Proceedings of the International Joint Conference on Neural Networks, 2021-July. [12] Wang, R., Huang, Z.,Liu, S.,Shao, H., Liu, D., Li, J., Wang, T., Sun, D., Yao, S., Abdelzaher,T. “DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction” (2021), SIGIR 2021 -Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, art. no. 3462934,pp. 163-172. [13] Konotopska,K., Iacca, G. “Graph-aware evolutionary algorithms for influence maximization” (2021),GECCO2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion, pp. 1467-1475. [14] Poquet,O. “Why birds of a feather flocktogether: Factors triaging students in online forums”(2021),ACM International Conference Proceeding Series,pp. 469-474. [15] Saxena, A., Kumar, B.,Gupta, A., Sahasrabudhe, N., Moharir, S. “Influencing Opinions of HeterogeneousPopulations over Finite Time Horizons” (2021), 2021 International Conference on COMmunication Systems and NETworkS,COMSNETS 2021, art. no. 9352905,pp. 474-482. [16] Wu, H., Wang, S., Fang, H. “LP-UIT: A Multimodal Framework for Link Prediction in Social Networks”(2021),Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021, pp. 742-749.
  • 15. REFERENCE S [17] Raza, M.R.,Hussain, W., Merigo,J.M. “Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU” (2021), Proceedings - 2021 Innovations in Intelligent Systems and Applications Conference,ASYU 2021. [18] Pereira, F.D.C.,De Rezende,P.J.,De Souza, C.C. “Effective Heuristics for the Perfect Awareness Problem”(2021), Procedia Computer Science, 195, pp. 489-498. [19] Ko, J., Lee, K., Shin, K., Park, N. “MONSTOR:An Inductive Approach for Estimating and Maximizing Influence over Unseen Networks” (2020), Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM2020, art. no. 9381460,pp. 204-211. [20] Khan, T., Michalas, A. “Trust and believe - Should we? evaluating the trustworthiness of twitter users” (2020), Proceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020, art. no. 9342970,pp. 1791-1800. [21] Yury,A., Andrade, E., Nogueira,M., Santos, A., Matos, F. “Social-based Cooperation of Vehicles for Data Dissemination of Critical Urban Events” (2020), 2020 IEEE GlobalCommunications Conference, GLOBECOM2020 - Proceedings,art. no. 9322301,. [22] Gaitonde, J., Kleinberg,J., Tardos, É. “Adversarial Perturbations of Opinion Dynamics in Networks”(2020),EC 2020 - Proceedings of the 21st ACM Conference on Economics and Computation, art. no. 3399490,pp. 471-472. [23] Bo, H., McConville,R., Hong, J., Liu, W. “Social Network Influence Ranking via EmbeddingNetwork Interactions for User Recommendation” (2020), The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020, pp. 379-384. [24] Pipergias Analytis, P., Barkoczi,D., Lorenz-Spreen,P., Herzog,S. “The Structure of Social Influence in Recommender Networks” (2020), The Web Conference 2020 - Proceedingsof the World Wide Web Conference, WWW 2020, pp. 2655-2661.
  • 16. REFERENCE S [25] Oriedi, D., De Runz, C., Guessoum,Z.,Younes,A.A., Nyongesa,H. “Influence maximization through user interaction modelling” (2020), Proceedings of the ACM Symposium on AppliedComputing, pp. 1888-1890. [26] Cuzzocrea,A., Leung, C.K.,Deng, D., Mai, J.J., Jiang, F., Fadda, E. “A combined deep-learning and transfer-learning approach for supporting social influence prediction” (2020), Procedia Computer Science, 177, pp. 170-177. [27] Trivedi, N.,Singh, A. “Efficient Influence Maximization in Social-Networks under Independent Cascade Model” (2020), Procedia Computer Science, 173, pp. 315-324. [28] Bhat, N.,Aggarwal, N.,Kumar, S. “Identification of Influential Spreadersin Social Networks using ImprovedHybrid Rank Method” (2020), Procedia Computer Science, 171,pp. 662-671. [29] Aldawish, R., Kurdi, H. “A ModifiedDegree Discount Heuristic for Influence Maximization in Social Networks” (2020), Procedia Computer Science, 170,pp. 311-316. [30] Mittal, D., Suthar, P., Patil, M., Pranaya, P.G.S.,Rana, D.P., Tidke, B.“Social Network Influencer Rank Recommender Using DiverseFeatures from Topical Graph” (2020), Procedia Computer Science, 167,pp. 1861-1871. [31] Luo, J., Liu, X., Kong, X. “Competitive opinion maximization in social networks” (2019),Proceedingsof the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM2019, pp. 250-257. [32] Sarkar, S., Shakarian, P., Armenta, M., Sanchez, D.,Lakkaraju, K. “Can social influence be exploited to compromise security: An online experimental evaluation” (2019),Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019,pp. 593-596.
  • 17. REFERENCE S [33] Zhang, Y. “Diversifying seedsand audience in social influence maximization” (2019),Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM2019, pp. 89-94. [34] Sarkar, S., Aleali,A., Shakarian, P., Armenta, M., Sanchez, D., Lakkaraju, K. “Impact of social influence on adoption behavior: An online controlled experimental evaluation” (2019),Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019,pp. 226-233. [35] Wu, L.,Hong, R., Sun, P., Wang, X., Fu, Y.,Wang, M. “A neural influence diffusionmodel for social recommendation” (2019),SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 235-244. [36] Eckles,D., Esfandiari, H., Mossel,E., Rahimian, M.A.“Seeding with costly network information”(2019), ACM EC 2019 - Proceedings of the 2019 ACM Conference on Economics and Computation, pp. 421-422. [37] Sarkar, S., Alvari, H., Shakarian, P. “Leveraging motifs to modelthe temporal dynamics of diffusionnetworks” (2019),The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019, pp.1079-1086. [38] Fish, B.,Friedler, S.A.,Bashardoust, A., Scheidegger,C.,Boyd,D., Venkatasubramanian, S. “Gaps in information access in social networks” (2019), The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, pp.480-490. [39] Wu, Q., He, P., Zhang, H., Weng, P., Chen, G.,Gao, X., Gao, H. “Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems” (2019),The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, pp. 2091-2102. [40] Zhang, D., Hou, J., Guo,T., Feng, Z.,Pan, H., Liang, Y.,Lin, H., Xia, F. “Judging a bookby its cover: The effect of facial perception on centrality in social networks” (2019),The Web Conference 2019 -Proceedings of the World Wide Web Conference, WWW 2019, pp. 2290-2300.