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Alessandro Vespignani

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4 papers
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4

AIJ Journal 2025 Journal Article

Human-AI coevolution

  • Dino Pedreschi
  • Luca Pappalardo
  • Emanuele Ferragina
  • Ricardo Baeza-Yates
  • Albert-László Barabási
  • Frank Dignum
  • Virginia Dignum
  • Tina Eliassi-Rad

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.

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IJCAI Conference 2025 Conference Paper

Human-AI Coevolution (Abstract Reprint)

  • Dino Pedreschi
  • Luca Pappalardo
  • Emanuele Ferragina
  • Ricardo Baeza-Yates
  • Albert-László Barabási
  • Frank Dignum
  • Virginia Dignum
  • Tina Eliassi-Rad

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i. e. , scientific, legal and socio-political.

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TCS Journal 2006 Journal Article

Exploring networks with traceroute-like probes: Theory and simulations

  • Luca Dall’Asta
  • Ignacio Alvarez-Hamelin
  • Alain Barrat
  • Alexei Vázquez
  • Alessandro Vespignani

Mapping the Internet generally consists in sampling the network from a limited set of sources by using traceroute-like probes. This methodology, akin to the merging of different spanning trees to a set of destination, has been argued to introduce uncontrolled sampling biases that might produce statistical properties of the sampled graph which sharply differ from the original ones. In this paper, we explore these biases and provide a statistical analysis of their origin. We derive an analytical approximation for the probability of edge and vertex detection that exploits the role of the number of sources and targets and allows us to relate the global topological properties of the underlying network with the statistical accuracy of the sampled graph. In particular, we find that the edge and vertex detection probability depends on the betweenness centrality of each element. This allows us to show that shortest path routed sampling provides a better characterization of underlying graphs with broad distributions of connectivity. We complement the analytical discussion with a throughout numerical investigation of simulated mapping strategies in network models with different topologies. We show that sampled graphs provide a fair qualitative characterization of the statistical properties of the original networks in a fair range of different strategies and exploration parameters. Moreover, we characterize the level of redundancy and completeness of the exploration process as a function of the topological properties of the network. Finally, we study numerically how the fraction of vertices and edges discovered in the sampled graph depends on the particular deployements of probing sources. The results might hint the steps toward more efficient mapping strategies.

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NeurIPS Conference 2005 Conference Paper

Large scale networks fingerprinting and visualization using the k-core decomposition

  • J. Alvarez-hamelin
  • Luca Dall'asta
  • Alain Barrat
  • Alessandro Vespignani

We use the k-core decomposition to develop algorithms for the analysis of large scale complex networks. This decomposition, based on a re- cursive pruning of the least connected vertices, allows to disentangle the hierarchical structure of networks by progressively focusing on their cen- tral cores. By using this strategy we develop a general visualization algo- rithm that can be used to compare the structural properties of various net- works and highlight their hierarchical structure. The low computational complexity of the algorithm, O(n + e), where n is the size of the net- work, and e is the number of edges, makes it suitable for the visualization of very large sparse networks. We show how the proposed visualization tool allows to find specific structural fingerprints of networks.

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v2026.09.13