Network Science Research
Bruno Gonçalves's network science research concerns the structure and dynamics of social systems: how connections form and dissolve over time, how information travels across them, and how collective attention rises and decays. With over 17,000 citations and an h-index of 44, this work has helped establish that when links change as fast as the processes running over them, the static network picture stops being an adequate approximation.
Research Areas
Time-Varying Networks
Networks whose links appear and vanish on the same timescale as the processes running over them — and the activity-driven framework that makes them analytically tractable.
Information Diffusion
How information spreads across a social network, which spreaders actually matter, and how diffusion in turn reshapes the network that carries it.
Collective Attention
The distinct dynamical classes by which topics rise and fall in public attention, and what social signals reveal before conventional reporting does.
Social Structure & Well-being
The cognitive limits on how many relationships people maintain online, and how emotional states correlate across social ties.
Activity-Driven Networks
Most network models treat the topology as fixed while a process — a rumour, an infection, a random walk — unfolds on top of it. That assumption breaks whenever links are created and destroyed on the same timescale as the process itself, which is the normal case for real social contact. The activity-driven framework instead characterizes each node by the rate at which it initiates connections, generating the temporal network from those activity rates. This makes the time-varying case analytically tractable, and it changes the answers: random walks explore such networks differently, and diffusion and search behave unlike their static-network counterparts.
Science of Science
A related strand turns these methods on scientific research itself — mapping how physics topics relate and evolve using machine learning over the research space (2019), and characterizing scientific production and consumption in physics (2013).
Key Publications
- Network Happiness: How Online Social Interactions Relate to Our Well Being
Bollen, Gonçalves. Complex Spreading Phenomena in Social Systems (2018)
- The happiness paradox: your friends are happier than you
Bollen, Gonçalves, van de Leemput, Ruan. EPJ Data Science (2017)
- The dynamics of information-driven coordination phenomena: a transfer entropy analysis
Borge-Holthoefer, Perra, Gonçalves, González-Bailón, Arenas, Moreno, Vespignani. Science Advances (2016)
- The Role of Information Diffusion in the Evolution of Social Networks
Weng, Ratkiewicz, Perra, Gonçalves, Castillo, Bonchi, Schifanella, Menczer, Flammini. KDD (2013)
- Emergence of influential spreaders in modified rumor models
Borge-Holthoefer, Meloni, Gonçalves, Moreno. Journal of Statistical Physics (2013)
- Activity driven modeling of time varying networks
Perra, Gonçalves, Pastor-Satorras, Vespignani. Scientific Reports (2012)
- Walking and searching in time-varying networks
Perra, Baronchelli, Mocanu, Gonçalves, Pastor-Satorras, Vespignani. Physical Review Letters (2012)
- Dynamical Classes of Collective Attention in Twitter
Lehmann, Gonçalves, Ramasco, Cattuto. WWW (2012)
- Beating the news using Social Media: the case study of American Idol
Ciulla, Mocanu, Baronchelli, Gonçalves, Perra, Vespignani. EPJ Data Science (2012)
- Modeling Users’ Activity on Twitter Networks: Validation of Dunbar’s Number
Gonçalves, Perra, Vespignani. PLOS ONE (2011)
- Happiness is assortative in online social networks
Bollen, Gonçalves, Ruan, Mao. Artificial Life (2011)
A complete list is available on the Publications page.
Related Research
Several threads that began as network science now have pages of their own: