Synthesizing Diverse Network Flow Datasets with Scalable Dynamic Multigraph Generation

Obtaining real-world network datasets is often challenging because of privacy, security, and computational constraints. In the absence of such datasets, graph generative models become essential tools for creating synthetic datasets. In this paper, we introduce a novel machine learning model for generating high-fidelity synthetic network flow datasets that are representative of real-world networks. Our approach involves the generation of dynamic multigraphs using a stochastic Kronecker graph generator for structure generation and a tabular generative adversarial network for feature generation. We further employ an XGBoost (eXtreme Gradient Boosting) model for graph alignment, ensuring accurate overlay of features onto the generated graph structure. We evaluate our model using new metrics that assess both the accuracy and diversity of the synthetic graphs. Our results demonstrate improvements in accuracy over previous large-scale graph generation methods while maintaining similar efficiency. We also explore the trade-off between accuracy and diversity in synthetic graph dataset creation, a topic not extensively covered in related works. Our contributions include the synthesis and evaluation of large real-world netflow datasets and the definition of new metrics for evaluating synthetic graph generative models.

Symbolic Regression with a Learned Concept Library

We present a novel method for symbolic regression (SR), the task of searching for compact programmatic hypotheses that best explain a dataset. The problem is commonly solved using genetic algorithms; we show that we can enhance such methods by inducing a library of abstract textual concepts. Our algorithm, called LaSR, uses zero-shot queries to a large language model (LLM) to discover and evolve concepts occurring in known high-performing hypotheses. We discover new hypotheses using a mix of standard evolutionary steps and LLM-guided steps (obtained through zero-shot LLM queries) conditioned on discovered concepts. Once discovered, hypotheses are used in a new round of concept abstraction and evolution. We validate LaSR on the Feynman equations, a popular SR benchmark, as well as a set of synthetic tasks. On these benchmarks, LaSR substantially outperforms a variety of state-of-the-art SR approaches based on deep learning and evolutionary algorithms. Moreover, we show that LaSR can be used to discover a novel and powerful scaling law for LLMs.

Neurosymbolic Grounding for Compositional World Models

We introduce Cosmos, a framework for object-centric world modeling that is designed for compositional generalization (CG), i.e., high performance on unseen input scenes obtained through the composition of known visual "atoms." The central insight behind Cosmos is the use of a novel form of neurosymbolic grounding. Specifically, the framework introduces two new tools: (i) neurosymbolic scene encodings, which represent each entity in a scene using a real vector computed using a neural encoder, as well as a vector of composable symbols describing attributes of the entity, and (ii) a neurosymbolic attention mechanism that binds these entities to learned rules of interaction. Cosmos is end-to-end differentiable; also, unlike traditional neurosymbolic methods that require representations to be manually mapped to symbols, it computes an entity's symbolic attributes using vision-language foundation models. Through an evaluation that considers two different forms of CG on an established blocks-pushing domain, we show that the framework establishes a new state-of-the-art for CG in world modeling.