If all the hub and authority scores are initialized to 1, what is the hub/authority score of a node after one iteration?
In the preceding discussion we encountered two recommended “hard constants” – the increment on te being ten times the last fetch time, and the number of back queues being three times the number of crawl threads. How are these two constants related?
How should the Boolean query x AND NOT y be handled? Why is naive evaluation
of this query normally very expensive? Write out a postings merge algorithm thatevaluates this query efficiently.
To answer these questions, let's break them down step by step:
1. If all the hub and authority scores are initialized to 1, what is the hub/authority score of a node after one iteration?
- To determine the hub/authority score of a node after one iteration, we need to understand the concept of the "HITS" algorithm. The HITS algorithm is an algorithm used to determine the relevance of web pages by calculating hub and authority scores. In each iteration of the algorithm, the hub and authority scores are updated based on the link structure of the web graph.
- In one iteration of the HITS algorithm, the hub score of a node is calculated as the sum of the authority scores of its neighbors, and the authority score of a node is calculated as the sum of the hub scores of nodes pointing to it.
- If all hub and authority scores are initialized to 1, after one iteration, the scores of each node will be updated based on the formula described above. The resulting hub and authority scores will reflect the importance of each node in the web graph.
2. In the preceding discussion, we encountered two recommended "hard constants" - the increment on te being ten times the last fetch time, and the number of back queues being three times the number of crawl threads. How are these two constants related?
- The relationship between the two constants mentioned is not explicitly stated in the given information. However, we can make some observations based on the context provided.
- The increment on te being ten times the last fetch time implies that the value of te (which is not explicitly defined) is increased by ten times the last fetch time. This suggests that the increment on te could be used to adjust and increase the value of te in each iteration.
- On the other hand, the number of back queues being three times the number of crawl threads suggests that the number of back queues should be a multiple of the number of crawl threads. This could be a design choice to optimize the efficiency of the back queues and the overall crawling process.
- Although it is unclear how these two constants are directly related, they both seem to involve adjusting parameters based on the previous iterations or the number of crawl threads.
3. How should the Boolean query x AND NOT y be handled? Why is naive evaluation of this query normally very expensive? Write out a postings merge algorithm that evaluates this query efficiently.
- The Boolean query x AND NOT y can be handled efficiently using a postings merge algorithm.
- Naive evaluation of this query can be expensive because it requires checking each document in the postings list for x and ensuring that it does not contain y. This involves iterating through both lists and performing a comparison for each document, resulting in a potentially large number of comparisons.
- To evaluate this query efficiently, we can use a postings merge algorithm that takes advantage of sorted postings lists. Here's an example algorithm:
1. Initialize two pointers, one for each postings list of x and y.
2. While both pointers are not at the end of their respective lists:
- If the document ID at the x pointer is equal to the document ID at the y pointer, move both pointers forward.
- If the document ID at the x pointer is less than the document ID at the y pointer, output the document ID and move the x pointer forward.
- If the document ID at the x pointer is greater than the document ID at the y pointer, move the y pointer forward.
3. If there are remaining document IDs in the x postings list, output them.
4. Return the list of document IDs as the result of the query.
- This algorithm takes advantage of the fact that the postings lists of x and y are sorted. By comparing the document IDs in a sequential manner, it efficiently identifies the document IDs that satisfy the query condition x AND NOT y.