The field of network research is moving towards a more nuanced understanding of the interplay between structural heterogeneity and functional fairness. Recent studies have highlighted the importance of decoupling these two concepts, revealing that structurally heterogeneous networks can exhibit high functional fairness under certain conditions. This shift in perspective has led to the development of new metrics and frameworks for evaluating network performance, such as the Network Imbalance metric, which quantifies the uniformity of connection experiences between all node pairs. Furthermore, there is a growing focus on robust anomaly detection strategies that account for both system vulnerabilities and evolving attack patterns, with approaches like causal graph profiling and training-free graph anomaly detection showing promise. Notable papers in this area include: Decoupling Structural Heterogeneity from Functional Fairness in Complex Networks, which introduces a new metric for assessing end-to-end accessibility fairness. Causal Graph Profiling via Structural Divergence for Robust Anomaly Detection in Cyber-Physical Systems, which proposes a causal graph-based anomaly detection framework for reliable cyberattack detection. FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection, which presents a novel training-free graph anomaly detection method. Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection, which proposes a framework for alleviating bias in autoencoder-based graph anomaly detection models.