Mean-field approximation for networks with synchrony-driven adaptive coupling.

Synaptic plasticity plays a fundamental role in neuronal dynamics, governing how connections between neurons evolve in response to experience. In this study, we extend a network model of θ-neuron oscillators to include a realistic form of adaptive plasticity. In place of the less tractable spike-timing-dependent plasticity, we employ recently validated phase-difference-dependent plasticity rules, which adjust coupling strengths based on the relative phases of θ-neuron oscillators.

Collective effect of self-learning and social learning on language dynamics: a naming game approach in social networks.

Linguistic rules form the cornerstone of human communication, enabling people to understand and interact with one another effectively. However, there are always irregular exceptions to regular rules, with one of the most notable being the past tense of verbs in English. In this work, a naming game approach is developed to investigate the collective effect of social behaviours on language dynamics, which encompasses social learning, self-learning with preference and forgetting due to memory constraints.

Sort & Slice: a simple and superior alternative to hash-based folding for extended-connectivity fingerprints.

Extended-connectivity fingerprints (ECFPs) are a ubiquitous tool in current cheminformatics and molecular machine learning, and one of the most prevalent molecular feature extraction techniques used for chemical prediction. Atom features learned by graph neural networks can be aggregated to compound-level representations using a large spectrum of graph pooling methods. In contrast, sets of detected ECFP substructures are by default transformed into bit vectors using only a simple hash-based folding procedure.

Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks.

Distributed ledger technologies have opened up a wealth of fine-grained transaction data from cryptocurrencies like Bitcoin and Ethereum. This allows research into problems like anomaly detection, anti-money laundering, pattern mining and activity clustering (where data from traditional currencies is rarely available). The formalism of temporal networks offers a natural way of representing this data and offers access to a wealth of metrics and models.

Broken detailed balance and entropy production in directed networks.

The structure of a complex network plays a crucial role in determining its dynamical properties. In this paper , we show that the the degree to which a network is directed and hierarchically organized is closely associated with the degree to which its dynamics break detailed balance and produce entropy. We consider a range of dynamical processes and show how different directed network features affect their entropy production rate.