book discussions

Author: alexeygrigorev

_books/20220919-kaggle-book.md

@@ -10,6 +10,228 @@ links:
   text: Book's page
 start: 2022-09-26 00:00:00
 title: Graph Algorithms for Data Science
+archive:
+- name: Luis
+  text: I am really looking forward to read this book. Looks super interesting. Does
+    it come with small projects?
+  replies:
+  - name: Tomaz Bratanic
+    text: Every chapter apart from the first 2 is a tutorial based project of its
+      own
+- name: Evren Unal
+  text: 'Hi Tomaz Bratanic,
+
+    I heard gnn here and there becoming more popular day to day.
+
+    Does it eventually take place of other deep learning algorithms like cnn and rnn?
+    or gnn is to use togeather with other algorithms?'
+  replies:
+  - name: Tomaz Bratanic
+    text: GNN is simply a CNN fitted to any type of graphs... if CNN can be used on
+      an image, which is a grid graph with predefined structure, that GNN is a variation
+      of CNN that can be used on any graph structure
+  - name: Tomaz Bratanic
+    text: other algorithms can be used to define features of nodes to be used in GNNs
+  - name: Evren Unal
+    text: 'As i understand gnn does not replace other algorithms.
+
+      I thing to better understand gnn one should see some examles.'
+- name: Ali Shakiba
+  text: 'Hi Tomaz Bratanic
+
+    Thanks for introducing the book.
+
+    Many graphs, such as the ones like Web connections or Twitter follower/following
+    graphs are very big, although they are sparse. Are Big graphs also covered in
+    your book, too? As far as I know, many problems in graph theory are NP-hard and
+    many of the polynomial-time algorithms are not useful for big graphs, even assuming
+    that something like Floyd-Warshall algorithm running on the graph corresponding
+    to the map of the World with all cities with O(n^3) running time is frightening.'
+  replies:
+  - name: Tomaz Bratanic
+    text: I don't deal with very large graphs in my book. For graphs even O(n^2) doesn't
+      scale well. Some algorithms have approximate variations. There are also other
+      algorithms that scale pretty well like PageRank, or Label Propagation etc...
+      In my book, I don't deal with pathfinding at all
+- name: Ashish Lalchandani
+  text: Hi  Tomaz Bratanic, thanks for being here! My question is, what are the applications
+    of graph algorithms in ML? I mean, what kind of problems in ML can be solved using
+    graph algorithms? Also, are the graph algorithms used in ML are same as the conventional
+    graph algorithms we use in competitive programming/leetcode?
+  replies:
+  - name: Tomaz Bratanic
+    text: More classical graph algorithms have been used to find the most important
+      nodes or find groups of nodes... Lately, there has been a shift into extracting
+      features from graphs and using them as inputs to ML models. I have no idea what
+      kind of graph algorithms are using in competitive landscape. Probably PageRank
+      is the most used algorithm out there
+  - name: Ashish Lalchandani
+    text: What kind of features are you referring to? Also, for graph algorithms in
+      competitive programming,  i was referring to BFD, DFS, backtracking, minimum
+      spanning tree, etc.
+  - name: Tomaz Bratanic
+    text: BFS and DFS are basis of some algorithms... I am talking more about unsupervised
+      graph algorithms like pagerank, label propagation, node2vec etc
+  - name: Tomaz Bratanic
+    text: There are a couple of features that you can extract based on the position
+      of the node in the network... how important it is, how well connected, how does
+      it group by, who are their neighbors etc...
+  - name: Ashish Lalchandani
+    text: Oh i see, that makes sense now, thanks for explaining! Much appreciated!
+- name: Bengsoon
+  text: "Hi Tomaz Bratanic thanks for writing this book. I am very new to this sub-space\
+    \ of AI ML. \nFrom my very shallow knowledge, graph theory as well as graph database\
+    \ have been around for a while, but I noticed that graph based ML etc has only\
+    \ risen to fame in the last few years. Is my observation right? If so, why is\
+    \ that?\nAlso, what are the practical strengths of graph based ML as well as the\
+    \ limitations, especially in production/deployment settings (compared to the conventional/mainstream\
+    \ ML algorithms like NN etc) ?"
+  replies:
+  - name: Tomaz Bratanic
+    text: 'Graph ML has risen to fame only in the last years, because most of the
+      graph ML algorithms have been developed in only the last couple of years, most
+      noticeably embedding and GNN models.
+
+      If relationships are predictable, then graph models can take those relationships
+      and use them as features in predictions whereas it is hard to encode those relationships
+      in traditional models'
+  - name: Tomaz Bratanic
+    text: 'take a look at pinterest for example: [https://medium.com/pinterest-engineering/pinsage-a-new-graph-convolutional-neural-network-for-web-scale-recommender-systems-88795a107f48](https://medium.com/pinterest-engineering/pinsage-a-new-graph-convolutional-neural-network-for-web-scale-recommender-systems-88795a107f48)'
+- name: Tomaz Bratanic
+  text: 'btw... I have tons of free articles on medium if you want to take a look
+    at free content before deciding about the book: [https://bratanic-tomaz.medium.com/](https://bratanic-tomaz.medium.com/)'
+  replies: []
+- name: Tomaz Bratanic
+  text: Ashish Lalchandani GerryK Luis You have been selected as the winners of the
+    free copy of the book. Please DM me and I will give you instruction to obtain
+    a free copy of the book
+  replies:
+  - name: Alexey Grigorev
+    text: Thanks for joining us this week!
+  - name: Tomaz Bratanic
+    text: My pleasure
+  - name: Ashish Lalchandani
+    text: "Thank you Tomaz Bratanic!! Thanks for answering our questions \U0001F600\
+      \ Thanks Alexey Grigorev Francis Terence Amit for hosting book of the week,\
+      \ much appreciated!"
+- name: shaolang
+  text: 'hi, Tomaz Bratanic
+
+    Congrats on launching the new MEAP!
+
+    Are graphs algorithms:
+
+    1. affected by the direction (bi- or uni-) of the edges? If so, what are the gotchas
+    we should be aware of, especially when dealing with uni-directional edges?
+
+    2. more effective than the "traditional" unsupervised learning algorithms for
+    clustering, k-means, etc., other than the fact that data in graphs don''t necessarily
+    need to conform to the same structure?
+
+    Recently, I''ve also come across another graph database -- Tigergraph -- that
+    touts itself more capable because of the number of hops it can make it much greater
+    than Neo4J, e.g., it can detect frauds from nodes/edges that are 6-8 hops away
+    from destination. If number of hops are really that important, are there algorithms
+    that can make up for such scenarios.'
+  replies:
+  - name: Tomaz Bratanic
+    text: 1. algorithms are definitely affected whether edges are undirected or directed.
+      You can think of a undirected edge as two directed edges, where each points
+      in the opposite direction. The main difference between undirected and directed
+      edges is the semantics... for example, if I am friends with you, does that directly
+      imply that you're also a friend with me. If the direct implication can be made,
+      then you are most likely dealing with an undirected edge. In practice you will
+      see a lot of undirected edges.
+  - name: Tomaz Bratanic
+    text: 2. Clustering is a big category of graph algorithms, so it's hard to say
+      if they are better. It has more of to do with your data input. If you are dealing
+      with vectors, you will most likely use something like k-means, but if you are
+      dealing with a connections between data points, then you might use something
+      like Label Propagation.
+  - name: Tomaz Bratanic
+    text: 3. detect frauds from nodes/edges that are 6-8 hops away from destination...
+      that's just marketing talk. Any database can do 6-8 hops or joins, even SQL.
+      The question is how fast and at what scale
+  - name: shaolang
+    text: 'thanks for taking my questions, Tomaz Bratanic!
+
+      1. As direction matters in edges, does that mean results from the algorithms
+      may differ depending on where the starting point is? Using your friend example
+      and assuming it''s unidirectional, the algorithm would be able to detect we
+      are friends when the query starts from you (node), but it can''t detect if the
+      query starts from me (node)?'
+  - name: shaolang
+    text: '(skipping 2)
+
+      3. Are you saying that Neo4J can do 6-8 hops too at reasonable speed and scale?
+      While I always take a pinch of salt, their marketing implies that Neo4J can''t
+      even complete the query. To make Neo4J complete this many hops, would we need
+      to write convoluted Cypher to achieve it?'
+- name: GerryK
+  text: 'Hi Tomaz Bratanic, thanks for being here.
+
+    - Are you refering to any tools for visualisation for better understanding the
+    graph concept?
+
+    - Do you see more and more projects/companies using graphs?'
+  replies:
+  - name: Tomaz Bratanic
+    text: '1. I don''t talk about viz tools in the book, but my favourite tool to
+      analyse and visualize small graphs is Gephi
+
+      2. I think that more and more companies are using graphs, some because they
+      see the value, some because it is become more and more of a "hot" technology'
+- name: Prashant Choudhary
+  text: 'Hi Tomaz Bratanic
+
+    ML models are probabilistic in nature. Using ML models to extract information
+    from unstructured text would not be 100% accurate. Mostly 80-90% accurate. In
+    contrast, Data in graphs should be factual and correct. Knowledge Graphs become
+    data source for various apps like goggle, chatbot where you need the information
+    to be factually correct. What are your thoughts?'
+  replies:
+  - name: Tomaz Bratanic
+    text: It depends on your use-case. The more messier your knowledge graph, the
+      messier the output. Extracting information from text is hard. First of all,
+      not all of the extracted information conforms to the graph structure, and secondly,
+      even 80-90% accuracy is sometimes hard to achieve. What might be a big problem
+      with constructing a graph from text is entity disambigation for example
+  - name: Taher Hassonjee
+    text: A little late to the conversation, but this is exactly what my company does.
+      We turn any unstructured text into a custom CSV output. If you're interested,
+      I'm happy to give you access and get your feedback
+  - name: Tomaz Bratanic
+    text: do you extract triples?
+  - name: Taher Hassonjee
+    text: Not yet but on the roadmap
+- name: insop
+  text: "Hi Tomaz Bratanic\nThank you for introducing [Graph Algorithms for Data Science](https://datatalks.club/books/20220926-graph-algorithms-for-data-science.html)\
+    \ , I would be very interested in reading recommendation and fraud detection chapters\
+    \ from your book.\n- one general question, what would be the difference that you\
+    \ are covering in your book and GNN (graph neural network) and graph CNN? \n-\
+    \ and what are the applications for each of those can be applied?\nThank you very\
+    \ much,"
+  replies:
+  - name: Tomaz Bratanic
+    text: 'GNNs are the state of the art methods of graph ML at the moment. My book
+      builds up all the knowledge to get to GNNs, but doesn''t delve too much into
+      them. If you are interested in recommendations and fraud detection I would recommend
+      the following book: [https://www.manning.com/books/graph-powered-machine-learning](https://www.manning.com/books/graph-powered-machine-learning)'
+  - name: Bhupendrasinh Thakre
+    text: Tomaz Bratanic do you also go through hands on learning in your book or
+      theory only
+  - name: Tomaz Bratanic
+    text: it's mostly hands on learning
+- name: Julius
+  text: The book that was mentioned in the week 1 video about being the best book
+    to read as regards DE. Please I didn't get the spelling
+  replies:
+  - name: Alexey Grigorev
+    text: 'You need to give more context. Which week 1? Was it course? Which one?
+
+      Also, this is not the right channel for asking these questions. Here we invite
+      book authors and ask them anything'
+
 ---
 
 Graphs are the natural way to understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment.