Quantifying the Dynamics of Network Detachment Across Scientific, Technological, Commercial, and Pharmacological Domains

Binglu Wang, Ching Jin, Chaoming Song, Johannes Bjelland, Brian Uzzi, Dashun WangBinglu Wang, Ching Jin, Chaoming Song, Johannes Bjelland, Brian Uzzi, Dashun Wang

Advanced Science

Despite the vast literature on complex network dynamics, our understanding of the network detachment process remains limited. This understanding is essential for deeper insight into network evolution. Here, we analyze four large-scale datasets that capture the structural patterns of detachment across scientific, technological, commercial, and pharmacological domains. Across these diverse domains, we uncover one simple pattern of preferential detachment in social systems, whereby the probability for individuals or organizations to disengage from a previously adopted practice correlates with the number of network neighbors who have already detached. To examine the structural consequences of this empirical pattern, we show that preferential detachment can fundamentally alter how networked systems disintegrate, inducing a phase transition in systems typically considered robust against detachment. We further derive an analytical framework to systematically understand the impact of preferential detachment on network dynamics, pinpointing the topological conditions under which it accelerates, decelerates, or leaves unchanged the breakdown of the network compared with random detachment. Together, these results demonstrate that the detachment dynamics follow simple yet reproducible patterns, deepening our quantitative understanding of detachment within networked social systems, with implications for the robustness and functioning of innovation communities.Despite the vast literature on complex network dynamics, our understanding of the network detachment process remains limited. This understanding is essential for deeper insight into network evolution. Here, we analyze four large-scale datasets that capture the structural patterns of detachment across scientific, technological, commercial, and pharmacological domains. Across these diverse domains, we uncover one simple pattern of preferential detachment in social systems, whereby the probability for individuals or organizations to disengage from a previously adopted practice correlates with the number of network neighbors who have already detached. To examine the structural consequences of this empirical pattern, we show that preferential detachment can fundamentally alter how networked systems disintegrate, inducing a phase transition in systems typically considered robust against detachment. We further derive an analytical framework to systematically understand the impact of preferential detachment on network dynamics, pinpointing the topological conditions under which it accelerates, decelerates, or leaves unchanged the breakdown of the network compared with random detachment. Together, these results demonstrate that the detachment dynamics follow simple yet reproducible patterns, deepening our quantitative understanding of detachment within networked social systems, with implications for the robustness and functioning of innovation communities.

Read the full article at: advanced.onlinelibrary.wiley.com

Endless Endings: The Many Forms of Death and What They Teach Us About Life, by Ricard Solé

While death may be one of life’s most predictable surprises, it has also proved itself to be one of the most fascinating and cutting-edge areas of scientific inquiry. Our bodies and lives tell a story of cellular creation and destruction that can be traced back to Earth’s primordial oceans. Yet when we follow the interwoven thread of beginnings and endings in an effort to discover death’s origins, nature, and meaning, we find that we cannot settle on a single definition of death or even a single interpretation of it. It would seem that there is not one death, but an endless variety of forms, each of which raises further questions about how well we understand death’s counterpoint—life.

Using death as a connecting theme, in Endless Endings, Ricard Solé explores the differing meanings of death in varied forms, reflecting on questions that are pushing forward the frontiers of scientific understanding. What is the physical basis of inevitable decay? Can we manipulate cellular rules to avoid death? What does death mean if life occurs inside a computer? Can a conscious machine save itself from death? Is immortality possible? What does it mean for language, thought, or information to die? As Solé reveals, life’s endings can be just as fascinating and varied as life itself.

More at: press.princeton.edu

Hiroki Sayama: Evoloops, Artificial Life, Complex Adaptive Networks, and Ideas as Living Organisms

Hiroki Sayama is a Japanese artificial life and complex systems scientist. He is a SUNY Distinguished Professor in the School of Systems Science and Industrial Engineering at Binghamton University, where he directs the Binghamton Center of Complex Systems (CoCo). Trained in information science at the University of Tokyo, he first made his mark in the field of artificial life with “evoloops,” the first self-reproducing cellular automaton loops shown to undergo genuine Darwinian evolution. He is the author of the freely available textbook “Introduction to the Modeling and Analysis of Complex Systems,” and over 260 peer-reviewed papers. He previously served as chief editor of the journal Complexity and as the vice president of the International Society for Artificial Life. A dedicated community builder, Sayama currently serves as the vice chair of the IEEE Computational Intelligence Society’s Task Force on Artificial Life and Complex Adaptive Systems and as an elected council, executive committee and CCS steering committee member of the Complex Systems Society. He also serves as vice president and board member of the Network Science Society, and founded the low-cost Northeast Regional Conference on Complex Systems and the open-access Northeast Journal of Complex Systems.

In this deep and far-reaching InterDialogue, recorded at the 2026 Conference on Complex Systems in Binghamton, which Sayama chaired, we trace the story of evoloops from John von Neumann’s self-reproducing automata to Christopher Langton’s loops to Sayama’s own pioneering work, before turning to his current research on open-ended artificial systems and the ongoing effort to define life itself. We explore the gap between the micro and the macro across disciplines, his view that life unfolds at every scale simultaneously, the Carl Sagan-inspired fascination with endless processes that has guided his career, and his work on adaptive networks, in which structure and dynamics co-evolve.

The conversation then turns to building open and accessible scientific communities, the value of interdisciplinarity, and the importance of listening when tackling real-world problems, as well as his surprising research showing that more isolated, locally connected “small world” networks can generate more diverse, higher-quality ideas than fully connected ones. We close with his concern that AI is homogenizing human thought, and his notion that ideas, and perhaps AI itself, are literally living, evolving organisms.

Watch at: www.youtube.com

A mechanism for collective foraging: percolating recruitment cascades coupled to a maximum-entropy field

Galen J. Wilkerson

Foraging recruitment is a contagion: an agent that finds a resource triggers a linear-threshold cascade, the model built for rumour and adoption spread on social networks, now embedded in space. Because activation is degree-normalized, the same rule that spreads the cascade also stifles it as neighbourhoods crowd, so the swarm aggregates on a resource and then releases to explore again. The cascade runs on the communication network, but acting on it moves agents in physical space, and that motion rewires the network: network and space are one coupled system, and this feedback, not any tuning, makes the coordination self-limiting. Because recruitment chains along the graph rather than reaching near neighbours alone, whether a discovery stays local or spans the swarm is a question of percolation. The swarm holds itself in a cascade window that is locally near-critical though globally subcritical, a self-organized, self-limiting drive-release cycle, sandpile-like but non-conservative. Nothing is tuned, yet behaviours that foraging biology documents separately emerge together: recruitment to discoveries, marginal-value patch-leaving, and Levy search with a switch to local exploitation. These follow from one least-committal principle, the resource field and the search both as maximum entropy, with Levy search matched to the field’s sparsity and recruitment to its clustering, so active-matter order and optimal foraging appear as one system seen in two domains. The gains are real but conditional: up to 7.6x the harvest of solitary search when resources are sparse and clustered, shrinking to nothing when search is already easy. That sparse, clustered field is also the generic one, because resources almost inevitably aggregate, so one untuned rule should recur widely.

Read the full article at: arxiv.org