Detecting Sequences and Cycles of Web Pages Narayan L. Bhamidipati and Sankar K. Pal Indian Statistical Institute Kolkata

Contents Introduction Objective Significance Procedure Experiments Future directions

The Web: A Directed Graph (V, A) Vertices  Web pages V = {v 1, v 2, …, v N } Arcs  Hyperlinks A = {e ij : v j  v i } Path: p 1.p 2. ….p n with arcs from p i to p i+1 Cycle: A Path with p n = p 1

Sequences of Web Pages Paths consisting of adjacent web pages Order sensitive A surfer may follow one such sequence when browsing pages

Cycles of Web Pages versities/ versities/ versities/United_States/ versities/United_States/

What are we looking for ? A particular kind of sequences and cycles Regular Consisting of similar units Units having similar relationship Reasonably sized

Why are these Sequences and Cycles Interesting ? Individual units form a single object These were intended to be together They collectively include the complete information Despite being part of a collection, individuality is maintained

Significance of Detecting Such Sequences and Cycles Compression Merge groups of pages Fewer pages  fewer links Pre-fetching Know where the surfer wants to be next Fetch the page(s) before being requested Saves time Errors: pre-fetching wrong pages

Significance of Detecting Such Sequences and Cycles (Contd.) Fair comparison Comparison independent of how content is presented Content split into multiple pages should be treated equivalent to the same in a single page Better retrieval Retrieval independent of the presentation Output a set of pages instead of a single one as a match

Fair Comparison

Improved Retrieval Retrieve only portions of interest Instead of, whole (huge) documents Avoid rewarding more content

How to Detect Sequences and Cycles of Web Pages ? Find navigational links Find consecutive pages Define what the elements of the sequence would satisfy Identify subsequences (or units) Concatenate Check for cycles

Finding Navigational Links: Background The purpose of a link may be Navigation Reference Advertisement Links between pages on the same server are treated as navigational Have also been treated as noise

Finding Navigational Links: Our Method Avoid treating links on the same server as navigational links Appear mostly either at the top or at the bottom Navigational links are generally huddled together Fewer text and images around such links

Advantages and Limitations Simple and fast Navigational links across servers are also identified Heuristics need not always work – fall back on sophisticated methods

Units of the Sequences A  B  C is a unit if C is “related” to B in the same way as B is “related” to A “related” is defined in terms of how they are linked Relation is stored as “position” of the link Several ways of defining “position”

Combining the units into sequences D  E  F B  C  D A  B  C C  D  E A  B  C  D  E  F

Cycle detection Existing cycle detection algorithms Cycle detection in number theory Special case of cycle detection in graph theory Stack based algorithm

Improvements and Speedups Believe the “rel” information provided by the (author of the) pages Use keywords like “next” and “previous” to perceive the relationships Utilize the information of the naming convention

Experimental Results Data Toy data: python tutorial in HTML Tutorial split into several chapters and sections Several cycles Mutilated data Certain pages deleted (missing links) 100% detection in all cases

Other experiments planned Real test: unorganized web pages Difficulties: Finding navigational links Noise (advertisements, etc) Dynamically generated Will the relationships hold ?

Leads us to … Concatenate detected sequences for analysis Modify retrieval mechanism Return sets of pages as results Improve mirror/duplicate detection

Future Work Consider other relations Unifying framework ? Improve identification of navigational links