Beyond Pairwise Network Interactions: Implications for Information Centrality

References

• Abbasi A, Parsons J, Pant G, Sheng ORL, Sarker S (2024) Pathways for design research on artificial intelligence. Inform. Systems Res. 35(2):441–459.Link, Google Scholar
• Acs ZJ, Braunerhjelm P, Audretsch DB, Carlsson B (2009) The knowledge spillover theory of entrepreneurship. Small Bus. Econom. (Dordrecht) 32(1):15–30.Crossref, Google Scholar
• Adams JS (2015) Equity theory. Organizational Behavior, vol. 1 (Routledge, New York), 134–158.Google Scholar
• Agarwal A, Leung ACM, Konana P, Kumar A (2017) Cosearch attention and stock return predictability in supply chains. Inform. Systems Res. 28(2):265–288.Link, Google Scholar
• Aksoy SG, Amburg I, Young SJ (2024) Scalable tensor methods for nonuniform hypergraphs. SIAM J. Math. Data Sci. 6(2):481–503.Crossref, Google Scholar
• Alexander M, Forastiere L, Gupta S, Christakis NA (2022) Algorithms for seeding social networks can enhance the adoption of a public health intervention in urban India. Proc. Natl. Acad. Sci. USA 119(30):e2120742119.Crossref, Google Scholar
• Andrei D, Hasler M (2015) Investor attention and stock market volatility. Rev. Financial Stud. 28(1):33–72.Crossref, Google Scholar
• Aral S, Van Alstyne M (2011) The diversity-bandwidth trade-off. Amer. J. Sociol. 117(1):90–171.Crossref, Google Scholar
• Baker M, Wurgler J (2006) Investor sentiment and the cross-section of stock returns. J. Finance 61(4):1645–1680.Crossref, Google Scholar
• Banerjee A, Chandrasekhar AG, Duflo E, Jackson MO (2013) The diffusion of microfinance. Science (1979) 341(6144):1236498.Google Scholar
• Barabási AL, Frangos J (2014) Linked: How Everything is Connected to Everything Else and What It Means for Business, Science, and Everyday Life (Basic Books, New York).Google Scholar
• Barber BM, Odean T (2008) All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Rev. Financial Stud. 21(2):785–818.Crossref, Google Scholar
• Battiston F, Amico E, Barrat A, Bianconi G, Ferraz de Arruda G, Franceschiello B, Iacopini I, et al. (2021) The physics of higher-order interactions in complex systems. Nature Phys. 17(10):1093–1098.Crossref, Google Scholar
• Benson AR (2019) Three hypergraph eigenvector centralities. SIAM J. Math. Data Sci. 1(2):293–312.Crossref, Google Scholar
• Bick C, Gross E, Harrington HA, Schaub MT (2023) What are higher-order networks? SIAM Rev. 65(3):686–731.Crossref, Google Scholar
• Borgatti SP (2005) Centrality and network flow. Soc. Networks 27(1):55–71.Crossref, Google Scholar
• Bothner MS, Podolny JM, Smith EB (2011) Organizing contests for status: The Matthew effect vs. the Mark effect. Management Sci. 57(3):439–457.Link, Google Scholar
• Bresman H, Zellmer-Bruhn M (2013) The structural context of team learning: Effects of organizational and team structure on internal and external learning. Organ. Sci. 24(4):1120–1139.Link, Google Scholar
• Burt RS (2004) Structural holes and good ideas. Amer. J. Sociol. 110(2):349–399.Crossref, Google Scholar
• Butler JS, Garg R, Stephens B (2020) Social networks, funding, and regional advantages in technology entrepreneurship: An empirical analysis. Inform. Systems Res. 31(1):198–216.Link, Google Scholar
• Cai H, Zheng VW, Chang KCC (2018) A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Trans. Knowledge Data Engrg. 30(9):1616–1637.Crossref, Google Scholar
• Carletti T, Battiston F, Cencetti G, Fanelli D (2020) Random-walks on hypergraphs. Phys. Rev. E 101(2):022308.Crossref, Google Scholar
• Carmi E, Oestreicher-Singer G, Stettner U, Sundararajan A (2017) Is Oprah contagious? The depth of diffusion of demand shocks in a product network. MIS Quart. 41(1):207–221.Crossref, Google Scholar
• Cencetti G, Battiston F, Lepri B, Karsai M (2021) Temporal properties of higher-order interactions in social networks. Sci. Rep. 11(1):7028.Crossref, Google Scholar
• Chen K, Li X, Luo P, Zhao JL (2021) News-induced dynamic networks for market signaling: Understanding the impact of news on firm equity value. Inform. Systems Res. 32(2):356–377.Link, Google Scholar
• Chi L, Ravichandran T, Andrevski G (2010) Information technology, network structure, and competitive action. Inform. Systems Res. 21(3):543–570.Link, Google Scholar
• Chitra U, Raphael B (2019) Random-walks on hypergraphs with edge-dependent vertex weights. Chaudhuri K, Salakhutdinov R, eds. Proc. 36th Internat. Conf. Machine Learn., vol. 97 (PMLR, New York), 1172–1181.Google Scholar
• Cook KS, Cheshire C, Rice ER, Nakagawa S (2013) Social exchange theory. Handbook of Social Psychology (Springer, Dordrecht, Netherlands), 61–88.Crossref, Google Scholar
• Cui P, Wang X, Pei J, Zhu W (2018) A survey on network embedding. IEEE Trans. Knowledge Data Engrg. 31(5):833–852.Crossref, Google Scholar
• Da Z, Engelberg J, Gao P (2011) In search of attention. J. Finance 66(5):1461–1499.Crossref, Google Scholar
• Das S, Huang X, Adeshina S, Yang P, Bachega L (2023) Credit risk modeling with graph machine learning. INFORMS J. Data Sci. 2(2):197–217.Link, Google Scholar
• Dhar V, Geva T, Oestreicher-Singer G, Sundararajan A (2014) Prediction in economic networks. Inform. Systems Res. 25(2):264–284.Link, Google Scholar
• Dong L, Jiang P, Xu F (2023) Impact of traceability technology adoption in food supply chain networks. Management Sci. 69(3):1518–1535.Link, Google Scholar
• Donick D, Lera SC (2021) Uncovering feature interdependencies in high-noise environments with stepwise lookahead decision forests. Sci. Rep. 11(1):9238.Crossref, Google Scholar
• Drake MS, Jennings J, Roulstone DT, Thornock JR (2017) The comovement of investor attention. Management Sci. 63(9):2847–2867.Link, Google Scholar
• Eom YH, Jo HH (2014) Generalized friendship paradox in complex networks: The case of scientific collaboration. Sci. Rep. 4(1):4603.Crossref, Google Scholar
• Fournet J, Barrat A (2014) Contact patterns among high school students. PLoS One 9(9):e107878.Crossref, Google Scholar
• Furnham A, Boo HC (2011) A literature review of the anchoring effect. J. Sociol. Econom. 40(1):35–42.Google Scholar
• Goel S, Goldstein DG (2014) Predicting individual behavior with social networks. Marketing Sci. 33(1):82–93.Link, Google Scholar
• Granovetter M (1978) Threshold models of collective behavior. Amer. J. Sociol. 83(6):1420–1443.Crossref, Google Scholar
• Gregor S, Hevner AR (2013) Positioning and presenting design science research for maximum impact. MIS Quart. 37(2):337–355.Crossref, Google Scholar
• Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 855–864.Google Scholar
• Guo L, Peng L, Tao Y, Tu J (2017) Joint news, attention spillover, and stock returns. Preprint, submitted March 7, http://dx.doi.org/10.2139/ssrn.2927561.Google Scholar
• Hahn J, Moon JY, Zhang C (2008) Emergence of new project teams from open source software developer networks: Impact of prior collaboration ties. Inform. Systems Res. 19(3):369–391.Link, Google Scholar
• Hayashi K, Aksoy SG, Park CH, Park H (2020) Hypergraph random-walks, laplacians, and clustering. Proc. 29th ACM Internat. Conf. Inform. Knowledge Management, 495–504.Google Scholar
• He J (2022) Executive network centrality and corporate reporting. Management Sci. 68(2):1512–1536.Link, Google Scholar
• Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Quart. 28(1):75–105.Crossref, Google Scholar
• Hirshleifer D (2015) Behavioral finance. Annual Rev. Financial Econom. 7:133–159.Crossref, Google Scholar
• Hochberg YV, Ljungqvist A, Lu Y (2007) Whom you know matters: Venture capital networks and investment performance. J. Finance 62(1):251–301.Crossref, Google Scholar
• Iacopini I, Petri G, Baronchelli A, Barrat A (2022) Group interactions modulate critical mass dynamics in social convention. Comm. Phys. 5(1):64.Crossref, Google Scholar
• Iyengar R, Van den Bulte C, Valente TW (2011) Opinion leadership and social contagion in new product diffusion. Marketing Sci. 30(2):195–212.Link, Google Scholar
• Jackson MO (2008) Social and Economic Networks (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
• Kane GC, Alavi M (2008) Casting the net: A multimodal network perspective on user-system interactions. Inform. Systems Res. 19(3):253–272.Link, Google Scholar
• Kane GC, Labianca G (2011) Is avoidance in health-care groups: A multilevel investigation. Inform. Systems Res. 22(3):504–522.Link, Google Scholar
• Kar AK, Dwivedi YK (2020) Theory building with big data-driven research: Moving away from the “what” towards the “why”. Internat. J. Inform. Management 54:102205.Crossref, Google Scholar
• Keck S, Tang W (2021) Elaborating or aggregating? The joint effects of group decision-making structure and systematic errors on the value of group interactions. Management Sci. 67(7):4287–4309.Link, Google Scholar
• Kumar A, Lee CM (2006) Retail investor sentiment and return comovements. J. Finance 61(5):2451–2486.Crossref, Google Scholar
• Lazer D, Pentland A, Adamic L, Aral S, Barabási AL, Brewer D, Christakis N, et al. (2009) Computational social science. Science (1979) 323(5915):721–723.Google Scholar
• Leng Y, Dong X, Moro E, Pentland A (2023) Long-range social influence in phone communication networks on offline adoption decisions. Inform. Systems Res. 35(1):318–338.Google Scholar
• Leng Y, Sella Y, Ruiz R, Pentland A (2020) Contextual centrality: Going beyond network structure. Sci. Rep. 10(1):1–10.Crossref, Google Scholar
• Leung ACM, Agarwal A, Konana P, Kumar A (2017) Network analysis of search dynamics: The case of stock habitats. Management Sci. 63(8):2667–2687.Link, Google Scholar
• Lin Z, Goh KY, Heng CS (2017) The demand effects of product recommendation networks: An empirical analysis of network diversity and stability. MIS Quart. 41(2):397–426.Crossref, Google Scholar
• Lin M, Lin M, Kauffman RJ (2012) From clickstreams to searchstreams: Search network graph evidence from a b2b e-market. Proc. 14th Annual Internat. Conf. Electronic Commerce (Association for Computing Machinery, New York), 274–275.Google Scholar
• Liu E (2019) Industrial policies in production networks. Quart. J. Econom. 134(4):1883–1948.Crossref, Google Scholar
• Liu X, Huang KW (2024) Controlling homophily in social network regression analysis by machine learning. INFORMS J. Comput. 37(3):684–702.Link, Google Scholar
• Liu Y, Pant G, Sheng OR (2020) Predicting labor market competition: Leveraging interfirm network and employee skills. Inform. Systems Res. 31(4):1443–1466.Link, Google Scholar
• Ludwig J, Mullainathan S (2024) Machine learning as a tool for hypothesis generation. Quart. J. Econom. 139(2):751–827.Crossref, Google Scholar
• Ma L, Krishnan R, Montgomery AL (2015) Latent homophily or social influence? An empirical analysis of purchase within a social network. Management Sci. 61(2):454–473.Link, Google Scholar
• Maass W, Parsons J, Purao S, Storey VC, Woo C (2018) Data-driven meets theory-driven research in the era of big data: Opportunities and challenges for information systems research. J. Assoc. Inform. Systems 19(12):1.Google Scholar
• McFowland E III, Shalizi CR (2021) Estimating causal peer influence in homophilous social networks by inferring latent locations. J. Amer. Statist. Assoc. 1:12.Google Scholar
• Medappa PK, Srivastava SC (2019) Does superposition influence the success of floss projects? An examination of open-source software development by organizations and individuals. Inform. Systems Res. 30(3):764–786.Link, Google Scholar
• Merton RK (1968) The Matthew effect in science: The reward and communication systems of science are considered. Science (1979) 159(3810):56–63.Google Scholar
• Nahapiet J, Ghoshal S (1998) Social capital, intellectual capital, and the organizational advantage. Acad. Management Rev. 23(2):242–266.Crossref, Google Scholar
• Nettasinghe B, Krishnamurthy V, Lerman K (2019) Diffusion in social networks: Effects of monophilic contagion, friendship paradox, and reactive networks. IEEE Trans. Network Sci. Engrg. 7(3):1121–1132.Crossref, Google Scholar
• Neuhäuser L, Scholkemper M, Tudisco F, Schaub MT (2023) Learning the effective order of a hypergraph dynamical system. Preprint, submitted June 2, https://arxiv.org/abs/2306.01813.Google Scholar
• Newman M (2018) Networks (Oxford University Press, Oxford, UK).Crossref, Google Scholar
• Oestreicher-Singer G, Sundararajan A (2012) Recommendation networks and the long tail of electronic commerce. MIS Quart. 36(1):65–83.Crossref, Google Scholar
• Peng G, Dey D (2013) Research note—A dynamic view of the impact of network structure on technology adoption: The case of OSS development. Inform. Systems Res. 24(4):1087–1099.Link, Google Scholar
• Peng L, Xiong W (2006) Investor attention, overconfidence and category learning. J. Financial Econom. 80(3):563–602.Crossref, Google Scholar
• Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. Proc. 20th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 701–710.Google Scholar
• Ren Y, Chen J, Riedl J (2016) The impact and evolution of group diversity in online open collaboration. Management Sci. 62(6):1668–1686.Link, Google Scholar
• Rossi AG, Blake D, Timmermann A, Tonks I, Wermers R (2018) Network centrality and delegated investment performance. J. Financial Econom. 128(1):183–206.Crossref, Google Scholar
• Rulke DL, Galaskiewicz J (2000) Distribution of knowledge, group network structure, and group performance. Management Sci. 46(5):612–625.Link, Google Scholar
• Santoro A, Battiston F, Petri G, Amico E (2023) Higher-order organization of multivariate time series. Nature Phys. 19(2):221–229.Google Scholar
• Schelling TC (1971) Dynamic models of segregation. J. Math. Sociol. 1(2):143–186.Crossref, Google Scholar
• Setia P, Rajagopalan B, Sambamurthy V, Calantone R (2012) How peripheral developers contribute to open-source software development. Inform. Systems Res. 23(1):144–163.Link, Google Scholar
• Shangguan W, Leung ACM, Agarwal A, Konana P, Chen X (2022) Developing a composite measure to represent information flows in networks: Evidence from a stock market. Inform. Systems Res. 33(2):413–428.Link, Google Scholar
• St-Onge G, Iacopini I, Latora V, Barrat A, Petri G, Allard A, Hébert-Dufresne L (2022) Influential groups for seeding and sustaining nonlinear contagion in heterogeneous hypergraphs. Comm. Phys. 5(1):25.Crossref, Google Scholar
• Stambaugh RF, Yuan Y (2017) Mispricing factors. Rev. Financial Stud. 30(4):1270–1315.Crossref, Google Scholar
• Stambaugh RF, Yu J, Yuan Y (2012) The short of it: Investor sentiment and anomalies. J. Financial Econom. 104(2):288–302.Crossref, Google Scholar
• Sundararajan A, Provost F, Oestreicher-Singer G, Aral S (2013) Research commentary—Information in digital, economic, and social networks. Inform. Systems Res. 24(4):883–905.Link, Google Scholar
• Tapiero CS (1979) A generalization of the Nerlove-Arrow model to multi-firms advertising under uncertainty. Management Sci. 25(9):907–915.Link, Google Scholar
• Tetlock PC (2007) Giving content to investor sentiment: The role of media in the stock market. J. Finance 62(3):1139–1168.Crossref, Google Scholar
• Tonellato M, Tasselli S, Conaldi G, Lerner J, Lomi A (2024) A microstructural approach to self-organizing: The emergence of attention networks. Organ. Sci. 35(2):496–524.Link, Google Scholar
• Tudisco F, Higham DJ (2021) Node and edge nonlinear eigenvector centrality for hypergraphs. Comm. Phys. 4(1):201.Crossref, Google Scholar
• Vogel EK, Awh E (2008) How to exploit diversity for scientific gain: Using individual differences to constrain cognitive theory. Curr. Directives Psych. Sci. 17(2):171–176.Crossref, Google Scholar
• Whitchurch GG, Constantine LL (1993) Systems theory. Sourcebook of Family Theories and Methods: A Contextual Approach (Springer, Boston), 325–355.Crossref, Google Scholar
• Zhang B, Pavlou PA, Krishnan R (2018) On direct vs. indirect peer influence in large social networks. Inform. Systems Res. 29(2):292–314.Link, Google Scholar
• Zhou J, Xu T, Chiao Y, Fang Y (2023) Interorganizational systems and supply chain agility in uncertain environments: The mediation role of supply chain collaboration. Inform. Systems Res. 35(1):184–202.Link, Google Scholar