Most of the recent discoveries in particle physics are linked to the increase of the detector volume and / or granularity to observe complex phenomena that were inaccessible previously due to a lack of precision. This approach increases the available statistics and precision by multiple orders of magnitude which facilitate the detection of rare events, at the price of a significant increase of the number of channels. The challenge is that most of the standard techniques for reconstruction and triggering are not operative in such a context. For example, the energy threshold-based triggers fail to handle the complexity of the high pile-up collisions. The neural network methods are known to handle well the noisy and complex data inputs to deliver high level classification and regression. In particular, the convolution techniques have allowed outstanding improvement in the computer vision field. Unfortunately, they do not cope with the very peculiar topologies of the particle detectors and the irregular distribution of their sensors. Alternatives have been discovered to obtain the same classification power in that kind of non-euclidean environment, for example, the spatial graph convolution which applies adapted convolution kernels to the data represented as an undirected graph labeled by the sensor measurements. These techniques have proven to give excellent results on the particle detector data at Large Hadron Collider but also for neutrinos experiments. They allow particle identification and continuous parameter regression, but also segmentation of entangled data which is a typical concern in secondary particle showers. The operations that transform the data into a graph are often very computationally expensive. In particular, all the techniques in which this operation is based on learned parameters (in the sense of machine learning) prevent the system from being used in a context where the computational time or latency are constrained (any triggering electronics, real-time data monitoring systems or even offline systems with a too big data volume). For example, in the Super-Kamiokande neutrino experiment, a complex shape identifier would advantageously replace the current energy cut during the reconstruction phase that rejects many low energy events despite their physical interest. Another example is the future high-granularity endcap calorimeter (HGCal) of CMS for which it becomes crucial to be able to extract high level trigger primitives directly from the electronics to handle the complexity of the high luminosity collisions and take accurate triggering decisions. This is why, it is of utmost importance to design high-performance versions of these algorithms, which can increase the performance in all the constrained situations and allow their realization in the detectors. The objective of this project is to develop and implement a new efficient selection algorithms for constrained computational environments by combining three main ideas • Reducing the graph construction complexity by developing algorithms based on pre-calculated graph connectivity which would allow obtaining an almost linear complexity for the online part by exploiting intrinsic parallelism of the problem. This is made possible by the fixed positionning of the sensors in the particle detectors. • Developing segmented version of graph convolution, allowing to distribute it over multiple computational unit. • Optimizing the size and the nature of the convolution networks with advanced techniques of derivative-free optimization and adaptation to the electronic implementation. These objectives will be declined in the three experiment contexts: Offline HGCal reconstruction, Online HGCal level 1 trigger and Super-Kamiokande reconstruction of the Diffused Supernova Neutrinos Background (DSNB).