Researchers demonstrated the mathematical impossibility of representing social networks and other complex networks using popular methods of'low-dimensional embeddings.' Models and algorithms for analyzing complex networks are widely used in research and affect society at large through their applications in online social networks, search engines, and recommender systems. According to a new study, however, one widely used algorithmic approach for modeling these networks is fundamentally flawed, failing to capture important properties of real-world complex networks. "It's not that these techniques are giving you absolute garbage. They probably have some information in them, but not as much information as many people believe," said C. "Sesh" Seshadhri, associate professor of computer science and engineering in the Baskin School of Engineering at UC Santa Cruz. Seshadhri is first author of a paper on the new findings published on March 2, 2020, in Proceedings of the National Academy of Sciences.

Models and algorithms for analyzing complex networks are widely used in research and affect society at large through their applications in online social networks, search engines, and recommender systems. According to a new study, however, one widely used algorithmic approach for modeling these networks is fundamentally flawed, failing to capture important properties of real-world complex networks. "It's not that these techniques are giving you absolute garbage. They probably have some information in them, but not as much information as many people believe," said C. "Sesh" Seshadhri, associate professor of computer science and engineering in the Baskin School of Engineering at UC Santa Cruz. Seshadhri is first author of a paper on the new findings published in Proceedings of the National Academy of Sciences.

Models and algorithms for analyzing complex networks are widely used in research and affect society at large through their applications in online social networks, search engines, and recommender systems. According to a new study, however, one widely used algorithmic approach for modeling these networks is fundamentally flawed, failing to capture important properties of real-world complex networks. "It's not that these techniques are giving you absolute garbage. They probably have some information in them, but not as much information as many people believe," said C. "Sesh" Seshadhri, associate professor of computer science and engineering in the Baskin School of Engineering at UC Santa Cruz. Seshadhri is first author of a paper on the new findings published March 2 in Proceedings of the National Academy of Sciences.

We investigate online convex optimization in changing environments, and choose the adaptive regret as the performance measure. The goal is to achieve a small regret over every interval so that the comparator is allowed to change over time. Different from previous works that only utilize the convexity condition, this paper further exploits smoothness to improve the adaptive regret. To this end, we develop novel adaptive algorithms for convex and smooth functions, and establish problem-dependent regret bounds over any interval. Our regret bounds are comparable to existing results in the worst case, and become much tighter when the comparator has a small loss.