Focus+Context Views of World-Wide Web Nodes Sougata Mukherjea and Yoshinori Hara, C&C Research Laboratories, NEC USA Inc., San Jose, Ca, USA E-mail: fsougata,
[email protected]
ABSTRACT
With the explosive growth of information that is available on the World-Wide Web, it is very easy for the user to get lost in hyperspace. When the user feels lost, some idea of the position of the current node in the overall information space will help to orient the user. Therefore we have developed a technique to form focus+context views of WorldWide Web nodes. The view shows the immediate neighborhood of the current node and its position with respect to the important (landmark) nodes in the information space. The views have been used to enhance a Web search engine. We have also used the landmark nodes and the focus+context views in forming overview diagrams of Web sites. World-Wide Web, Landmarks, Information Visualization, Overview Diagrams. KEYWORDS:
1 INTRODUCTION
A major problem in the World-Wide Web is being lost in hyperspace: because there are no restrictions on how users navigate through the information space, they can become disoriented while navigating. If, while surfing through the Web, the user comes to a particular node and feels lost, some idea of the position of the node in the overall information space will help to orient the user. Similarly, when the user specifies the URL to jump to a particular node using a Web browser, some information about the node’s position will be very useful. This paper discusses a technique to develop focus+context views of Web documents. The view shows the details of a particular node; nodes in the immediate neighborhood, those that can directly reach and can be reached from the document are shown. While the local detail is useful, the user also needs to understand the global context. Since the overall space is a large and complicated network, the paths to and from the important (landmark) nodes are only shown. This
is similar to a common geographical navigation strategy: a lost person will try to find where she is using her immediate neighborhood and important geographical landmarks. Another popular approach to reduce the lost in hyperspace problem is to present an overview diagram of the space. These diagrams present the structure of the underlying information space and let users see where they are, what other information is available and how to access it. They are useful tools for orientation and navigation in hypermedia documents [22]. However, for any real-world hypermedia system with many nodes and links like the World-Wide Web, the overview diagrams represent large complex network structures. They are generally shown as 2D or 3D graphs and comprehending such large complex graphs is extremely difficult. The layout of graphs is also a very difficult problem [2]. Other attempts to visualize networks such as Semnet [6], have not been very successful. Therefore, we have developed a technique of presenting an overview of an information space with respect to the landmark nodes. Since these are the important nodes in the space, they allow the user to gain an understanding of the contents of the space. Using the geographical navigation metaphor, this is like knowing a city by learning about its landmarks. Since the number of landmark nodes is small, this overview diagram is not very complicated. The next section presents related work. Section 3 discusses the method for finding landmark nodes. Section 4 explains the technique for forming the focus+context views. Section 5 gives examples to show how such views enhance the usefulness of Web search engines. Section 6 discusses the use of landmark nodes and focus+context views to form overview diagrams of sections of the WWW. Finally section 7 is the conclusion. 2 RELATED WORK
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A popular approach for visualizing abstract information is to use focus+context techniques by which the information of interest to the user is shown in detail, smoothly integrated with other context information. By balancing local detail and global context, this technique can display information at multiple levels of abstraction simultaneously. An example of this technique is the fisheye-view concept [7]. The
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focus+context technique has been used to develop visualizations of different data organizations. Examples include perspective walls [12] for visualizing linear data; document lens [19], a 3D visualization strategy for textual documents; table lens [18], a method for presenting tabular information and hyperbolic browser [11] for visualizing trees. Instead of developing focus+context views of a data structure, this paper presents a strategy of showing the local details and the global context of WWW nodes. This work is also related to research that tries to extract useful structures from or reduce the complexity of hypermedia networks. For example, clustering has been used before to reduce the complexity of hypertext networks. In some cases [8], the hypertext is considered as a set of documents and clustering algorithms based on textual analysis are used. In other cases [9], the similarity measure for clustering is based on the graph structure and graph algorithms are used for clustering. Hybrid approaches also exist [15]. Another approach is to identify hierarchies from more complex hypermedia networks [16]. An interesting approach is presented in [17] which utilizes both the topology and textual similarity between nodes as well as usage data collected by servers and WWW page meta-information like title and size to extract usable structures from the World-Wide Web. Linear equations and spreading activation models are employed to arrange Web pages based upon functional categories, node types and relevancy. Like these previous research activities, our work is also trying to identify useful structures to let the user have a better understanding of complicated hypermedia networks. However, unlike the previous works, we try to identify the useful structures with reference to any particular node rather than the overall information space. Various systems have been developed that try to visualize sections of the World-Wide Web using overview diagrams. Examples include the Navigational View Builder [14] and Narcissus [10]. Since visualizing large, complicated hypermedia networks is difficult, these systems adopt various techniques to form understandable views. Our approach of using landmarks to form an overview of the information space is another such technique. We use the landscape metaphor to visualize the information space in the overview diagrams. This is motivated by the File System Navigator [21], which utilize a 3D landscape to show a file system hierarchy. The Harmony Internet Browser [1], which runs as a client for the Hyper-G hypermedia information system [13], also uses this metaphor. The richness of the Hyper-G data model provides plenty of scope upon which to extract simplified substructures to form understandable visualizations: hierarchical structures, bidirectional hyperlinks and search and retrieval facilities. On the other hand, we are assuming a simple node and link data model and to avoid showing a large complicated graph in the overview, we only
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show the landmark nodes and the links between them. The landscape metaphor allows spatial navigation through a hypertext system; thus our approach is similar to the information city project [5] which uses the metaphor of a city to represent documents as buildings in a virtual environment through which the user can navigate. 3 LANDMARK NODES
We assume the scenario that the user is navigating in some web locality, meaning some collection of WWW pages. Generally, these would be pages at some particular physical site or WWW server. The landmark nodes for the web locality are calculated by using three metrics; a connectivity metric indicating how a node is connected to other nodes in the information space, an access-pattern metric indicating how many times the node has been accessed in recent times and a depth metric indicating at what depth the node resides in the file system hierarchy of the Web locality. 3.1 Building a database for a Web locality
To find out the connectivity of a node, the topology, which is the hyperlink structure among WWW pages at a Web locality needs to be extracted. We use Harvest Information Discovery and Access System [4] for this purpose. Harvest indexes all Web pages starting from a given URL. Various methods for limiting the pages that are indexed, for example by using regular expressions, are possible. Moreover, Harvest extracts the linking information for the pages and this can be used to extract the topology. Note that, extracting the topology using Harvest is a time consuming process (several hours for a large site). It also needs to be repeated if the site’s topology changes. The landmark node calculation method also uses the access frequency for the Web pages. This information is available by analysis of the log files. For each node we calculate the number of times it was accessed in the preceding month. To keep the access frequency uptodate this process needs to be done periodically. We have gathered information for several Web localities, but in this paper, we focus on the pages served by the College of Computing, Georgia Institute of Technology Web server (URL: http://www.cc.gatech.edu). We used regular expressions to limit the indexing to textual nodes. Moreover, some sections of the server (for example, those dealing with continuing education courses at the College) were filtered out. Altogether about 2500 Web pages were indexed. 3.2 Discovering Landmark Nodes
Finding nodes that are good landmarks is not a trivial task. Therefore, we use three heuristic metrics for determining landmark nodes.
Connectivity: Valdez and Chignell [23] anticipated that “landmarks would tend to be connected to more objects than nonlandmarks, in
the same way that major hubs serve as landmarks in airline systems.” While running some experiments they observed a high correlation between the recall of words in a hypertext (how easily those words were remembered by subjects) and their second-order connectedness. The second-order connectedness is defined as the number of nodes that can be reached from a node by following at most two links. As observed in [3], since hypertexts are directed graphs, it is possible to extend the idea and postulate that nodes that have high back second-order connectedness are also good landmarks. The back second-order connectedness of a node is the number of nodes that can reach the specified node in at most two steps. Similarly, the number of nodes that can be reached from the node by following at most one link (the outdegree of the node) and the number of nodes that can reach the node following at most one link (the indegree) should be also used in calculating the importance of the node.
Access frequency: However, a major limitation of this approach is that it uses just structural analysis for determining the importance of the nodes. This can sometimes lead to unexpected results. For example, it is now a common practice to make manuals available on the Web. Each section of the manual becomes a Web page and there is a Table of Content page that links to and is linked from all these pages. For some large manual with lots of sections, the Table of Content will have a high connectivity and thus high importance. However, considering the overall Web locality, this page may not be that important. Similarly, an important node in the information space may not be a landmark since it may not be connected to many nodes. To prevent these false positive and negative cases, other metrics are also needed for determining landmarks. Therefore, the access frequency of the node is also used to calculate the importance of the nodes. Obviously, nodes that have been accessed many times are very important. Depth: Generally, when a Web site is developed, the file system hierarchy is formed so that the important nodes are higher up in the hierarchy. For example, in the College of Computing site at Georgia Tech, the top directory of the Web site has the home page of the college as well as sub directories for the different research areas, home pages of the people in the college, etc. Each research directory in turn has a Web page describing the overview of that research area and sub directories for the different projects. The people directory has sub directories for faculty, students, staff, etc. Thus, the pages that give more general information and therefore more important for understanding a Web locality are higher up in the hierarchy than the pages containing detailed information. Because of this, the depth of a node can also be used to determine the importance; the lesser the depth the more important is the node. The depth can be determined from the URL.
For example, http://www.cc.gatech.edu/ has a depth of 1 (the lowest possible value) while http://www.cc.gatech.edu/gvu/ people/Phd/Sougata.Mukherjea.html has a depth of 4. Thus, the procedure for discovering landmarks can be summarized as follows: 1. Let: SOC = second-order connectedness BSOC = back second-order connectedness I = indegree O = outdegree A = access frequency D = depth 2. Calculate:
struct importance = (I + O) * wt1 + (SOC + BSOC) * wt2 where wt1 + wt2 = 1. The importance of the nodes by structural analysis is shown by struct importance. The parameters wt1 and wt2 are the weights of the first-order and
second-order connectedness respectively. These parameters can be controlled by the user. By default, the weight of firstorder connectedness is a little more than the weight of the second-order connectedness since nodes that are connected to more nodes directly should be more important.
3. Calculate:
importance = struct importance/(Max struct importance) * conn wt + A/(Max A) * access wt + depth wt/D where conn wt + access wt + depth wt = 1. The overall importance of a node is shown by importance. It is a number between 0 and 1. The parameters conn wt, access wt and depth wt, determining the importance of connectivity, access frequency and depth respectively can be controlled by the user.
4. Iff importance > cutoff, the given node is a landmark where cutoff is a value that can be changed by the user. By default, a value of 0.1 is chosen for cutoff. 3.3 Changing the Parameters for Landmark Calculation
If the parameters that are used in the landmark calculation process are changed, the focus+context views of the nodes will be modified. If the cutoff value to determine the landmark nodes is increased, the number of landmark nodes is decreased. For example, if the value of cutoff is increased to : from the default : , only two nodes, http://www.cc.gatech. edu/ and http://www.cc.gatech.edu/gvu/gvutop.html remain as landmarks in the Georgia Tech College of Computing server. (In the default case there are 42 landmark nodes). The number of nodes in the context views would obviously be reduced. Figure 1 shows the effect of changing the cutoff value on the number of landmark nodes.
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Figure 1: The effect of the cutoff value on the number of landmark nodes. As the value increases, the number of landmarks decreases rapidly.
Unfortunately, for each web locality the Web administrator may need to tune the cutoff value so that only the nodes that seem to be logically important are chosen to be landmarks by the algorithm. This is because the importance of the nodes will be influenced by the characteristics of the space. For example, if for a WWW site many nodes have high access frequency and high connectivity, all of them would have a large value for importance. Therefore, a high value of cutoff is needed so that only a few nodes are chosen as landmarks by the algorithm. (If a lot of nodes are designated as landmarks the focus+context view would become very complicated and the advantages of this method would be lost). It might be interesting to allow the user to change the cutoff value using a slider and see the changes to the views dynamically. Of course, filtering based on the various node attributes like access frequency and indegree would also be very useful to gain an understanding of a Web locality; for example, the user may want to only look at nodes which have been accessed more than 1000 times or nodes which are not connected to any other nodes. Instead of using a cutoff value to determine landmarks, there are two other options. Firstly, the n most important nodes could be chosen as landmarks. For this option also the Web administrator may need to tune the value of n since the number of landmark nodes should not be totally independent of the size of the web locality. Another option is to choose the top n of the nodes (based on importance) as landmarks. However, in this case also the value of n may have to be tuned to prevent very few or a large number of nodes becoming landmarks. Note that using just a cutoff value to determine landmarks is much simpler than these two methods since a procedure is not required to find the important nodes.
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If the weights determining the relevance of the structural analysis, access frequency and depth are changed, the importance of the nodes change depending on their depth, access frequencies and connectivity. For example, if only access frequency is used, the node http://www.cc.gatech.edu/grads/ b/Gary.N.Boone/beauty and love.html becomes very impor-
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tant since it has a high access frequency. Increasing the weight of structural analysis however reduces the node’s importance since it is not connected to many nodes. Depth of a node is determined mainly by a few people who are the administrators of the Web site. On the other hand the connectivity of a node is determined by all the people of the site. A person will link to a page that she considers to be important. However, the access frequency of a node is determined by the largest group of people - the global Web community. Therefore, by default, access wt is assigned 0.5, conn wt 0.4 and depth wt 0.1. 4 DEVELOPING FOCUS+CONTEXT VIEWS
The focus+context view helps in determining the position of a node in the Web locality. It consists of the in-view and the out-view.
In-view: In-view shows the paths leading to the node of interest. Developing the in-view consists of finding out the local details and the global context. – Focus or the local details: To understand the position of a particular node in the Web locality, the user would like to see the details of the node’s immediate neighborhood. Therefore, we show the nodes that are directly linked to the particular node. – The global context: If only the local details are shown, the user does not have any understanding of the position of the particular node in the overall global context. However, showing all the nodes in the web locality would make the views too complicated for easy understanding. Therefore the global context only shows the relationship of the given node to the landmark nodes. For each landmark node, we first calculate the shortest path from it to the given node. If the path does not exist, then the landmark node is not related to the node of interest and therefore not shown in the in-view.
Even if the path exists, few of these paths may not be useful for understanding the position of the node in the information space. For example, for some landmark nodes, the shortest paths from them to the given node is via a node with very high connectivity. Thus, for the College of Computing Web server, the shortest paths from some of the landmarks nodes to a given node x, are shortest paths from the landmark nodes to nodes with very high connectivity like gvutop.html, and then the shortest path from these nodes to x. In these cases, the landmark nodes have no direct relationship to x and the shortest path from them to gvutop.html will not be useful to understand the position of the given node x in the information space. Note that the shortest paths from the nodes of very high connectivity to x will always be shown in the view. Therefore, the shortest path from a landmark node to any node is shown in the in-view if and only if for all links in the path, except links whose destination is the given node, the importance of the source of the link is significantly greater than the importance of the destination (a value of 0.25 is chosen by default). This would prevent paths from landmark nodes to x via nodes of high connectivity (and importance). However, shortest paths from a landmark node to x, where for a link in the path the source importance is slightly lower than the destination importance, would be shown in the view. Note that the shortest paths from a landmark node to a node of very high connectivity (like gvutop.html) would be useful when these nodes are the nodes of interest. Therefore, we do not use the restriction of the link source importance being much greater than the destination importance for links whose destination is the node of interest.
Out-view: Out-view shows the paths from the node of interest. The outview also consists of the local details and the global context. – Focus or the local details: The local details shows the links from x. – The global context: The global context consists of the shortest paths to the landmark nodes from the given node provided the path exists and the destination of all links in the path (except those whose source are the node of interest) have importance greater than that of the source of the links by a threshold value.
4.1 Complexity of the method
After the topology is extracted, finding the nodes to and from a given node is trivial. Breadth-first search is used to find the shortest path between two nodes. The complexity is O n l where n is the number of nodes and l is the number of links. Therefore, if there are k landmarks, the method to find node context is O n l k . Since k is small, this method is computationally quite cheap.
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5 ENHANCING A WEB SEARCH ENGINE
With the explosive growth of information that is available through the World-Wide Web, it is becoming increasingly difficult for the users to find the information of interest. Therefore, various searching mechanisms that allow the user to retrieve documents of interest are becoming very popular and useful. Most large Web sites allows the user to retrieve Web pages of interest in the site by specifying keywords. However, while these search engines show the Web pages that match the user’s queries, they don’t give any indication of the actual position of the page in the overall Web locality. We are developing a Multimedia Web search engine that allows the searching of Web localities based on both images and keywords. To allow the user to gain an understanding of the position of the retrieved nodes in the Web locality, focus+context views are used. Figure 2 shows the results of searching the Georgia Tech College of Computing Web site for the word “sougata”. Several pages are retrieved, but looking at the result does not give any indication of the position of the pages in the overall information space. Figure 3 shows the focus+context view of the retrieved page, http://www.cc.gatech.edu/gvu/people/Phd/Sougata.Mukherjea. html. (This is the home page of the first author of this paper.) Virtual Reality Modeling Language (VRML) is used for the visualization. The node whose view is being shown is depicted as a sphere while the other nodes are depicted as cubes. The size is directly proportional to the importance of the node. Landmark and non-landmark nodes are depicted by different colors (red and green respectively). Links in the in-view are represented by blue and the links in the out-view by cyan. We have developed a simple layout algorithm that shows the paths in the in-view to the left of the node of interest and the paths in the out-view to the right. Note that a node may occur in both the in-view and the out-view. This focus+context view shows the immediate neighborhood of the node as well as shortest paths from several landmark nodes like gvutop.html. The view is quite useful in undersanding the position of Sougata in the College of Computing. Since there is a path from alumni, it shows that he is an alumni of the College; a path from index.grad shows that he was a graduate student and the path from gvutop shows that his research was in the gvu (Graphics, Visualization & Usability) area. Moreover, some related nodes (like the homepage of his advisor, James.D.Foley, and pages describing some projects that he had worked on, Nvb and visdebug) can be seen. Note that clicking on the node whose view is being shown retrieves the actual page while clicking on some other node retrieves the focus+context view of that page. This allows the user to quickly gain an understanding of the section of the Web that is of interest.
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Figure 2: The results of retrieving documents with the word “sougata” from the Georgia Tech College of Computing Web site. Several pages are retrieved, but there is no indication of the position of the pages in the overall information space.
Figure 3: Focus+Context view of the homepage of Sougata Mukherjea in the College of Computing Web site. It is useful in understanding the first author’s position in the college.
Figure 4: Focus+Context view of a page describing a research project. Various important information about the project are apparent.
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As another example, let us look at the focus+context view of http:/www.cc.gatech.edu/gvu/animation/Areas/secondary/ Water/water.html as shown in Figure 4. The view also helps the user to understand various information about the node. It can be seen that the page describes a research project in the animation area of GVU since it has a path from gvutop via research, a page giving an overview of research in GVU and animation, the main page of the animation area. Because of the path from biox, a page describing the Biocomputing area, it is apparent that this project is also of interest of researchers in that area. Finally, the page also links to the homepage of the main faculty involved in the project, Jessica Hodgins. 6 OVERVIEWS OF WEB LOCALITIES
Searching is appropriate when the user has a well-defined understanding of what information is needed. However, in many cases the user is not certain of exactly what information is desired and needs to learn more about the content of the Web locality. In this case browsing is an ideal navigational strategy. For browsing, we need to present to the user a visualization of the underlying information space, that is an overview diagram. However, if we show all the nodes and links of a Web locality, the overview diagram becomes too complex to be really useful. Therefore, we visualize the Web locality through landmark nodes and focus+context views. Figure 5 shows the overview of the Georgia Tech College of Computing Web server. We utilize various features of a geographical map for the overview diagram. Just as a map won’t show all the details of a city, but only the important landmarks, in our visualization only the landmark nodes are shown. The landmark nodes and the links between them are arranged in a landscape. (We utilize a modified form of a force-based graph layout algorithm [20] for an aesthetic layout). Like a map, the more important landmarks are displayed more prominently. Thus, the height of the cubes representing the nodes represent the importance of the nodes. The colors represent the access frequency. Nodes with less access frequency are darker. The visualizations are developed using VRML. Thus they are platform independent and can be viewed in any VRML viewer. We have used SGI’s Cosmp Player both on an Unix Workstation and a PC. The VRML browser provides various methods of navigating through the 3D space and thus gain an understanding of the Web locality. If the user clicks on a node, its focus+context view is shown as another VRML world. The overview diagram is useful in various ways. It allows the user to quickly gain an understanding of the important nodes in the Web locality. Thus from Figure 5, the user can understand that some of the important nodes are www.cc.gatech.edu and gvutop. The user can also see the nodes that have been accessed very often. The user may also get some unexpected informa-
tion. For example, one of the most accessed pages is one which has a collection of poems (http://www.cc.gatech.edu/ grads/b/Gary.N.Boone/beauty and love.html). One would not be generally searching for poems in the Web site of a Computer Science department! Finally, the user can click on a node to see its focus+context view. This allows the user to understand the position of a particular landmark node. For example, in Figure 6 the user is examining the focus+context view of beauty and love.html. The user can navigate through the Web locality by moving from the overview diagram to the focus+context view of a landmark node of interest to the view of another node shown in the previous view and so on. The actual Web page can also be retrieved. This alternative method of browsing may help the user to quickly get an idea of the information contained in a Web locality. Since both the overview diagram and the focus+context views are simple, the user can comprehend all the information quite easily. 7 CONCLUSION
The following are the major contributions of this work:
We have formalized the notion of landmark nodes in the World-Wide Web. A technique for developing focus+context views of WWW nodes is presented. The technique is computationally inexpensive and the views can be utilized in various ways. Examples are used to illustrate the usefulness of the technique to enhance a Web search engine. We have used landmark nodes for developing overview diagrams of Web localities using a geographical landscape metaphor. The overview diagrams and the focus+context views presents an alternative method of browsing a Web locality. Future work is planned along the following directions:
Developing a Web Site Management Tool: A major limitation of our approach is that a time consuming pre-processing step is required before the navigational assistance offered by the visualizations is available. Therefore, users casually browsing a series of sites are unlikely to benefit from the approach. We hope that just as many Web sites are nowadays providing a searching mechanism for their sites, in the near future most sites will be also offering various kinds of visualizations to help users navigate effectively through their Web locality. To enable the Web masters to easily develop useful navigational aids for their Web sites, we are developing a Web site management tool. This tool will allow the semi-automatic development of both a querying interface for a Web site as well as various visualizations to facilitate browsing. The system will also allow the views to be updated periodically to reflect various changes in the Web site (like the site’s topology).
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Figure 5: An overview diagram of the College of Computing Web server based on a geographical landscape metaphor. Only the landmark nodes are shown. Their heights are directly proportional to the nodes’ importance. The access frequency determines the color; nodes with less accesses are darker.
Figure 6: The focus+context view of a landmark node. The user can click on the landmark node in the overview diagram of figure 5 and jump to this view.
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Finding Similar Nodes: When the user is at a node of interest, she would also like to look at other similar nodes. While such nodes may already exist in the focus+context view, this may not always be the case. Therefore, the focus+context view should also show other similar Web pages. However, finding nodes that are similar to a given node is not a trivial task. Various approaches are possible. Similarity can be determined by textual analysis. Techniques from information retrieval can be applied to calculate a text similarity matrix which represents the inter-document text similarities among WWW pages. After Harvest has indexed the pages in a Web locality, information about the keywords that are present in the various Web nodes is available. Using this information, a vector can be built for each node where each component of the vector represents a keyword. Entries in the vector for a Web page indicate the presence or frequency of a keyword in the page. For each pair of pages (or nodes), the dot product of these vectors produce a similarity measure which can be entered into the text similarity matrix. If a node is not already in the focus+context view and its similarity measure is greater than a cutoff value, it should be also included in the view. Note that the formation of the text similarity matrix is quite complex but is needed to be computed only one time when Harvest indexes the Web locality and the topology is extracted. We are also exploring the possibility of finding the similar HTML pages by examining the images contained in them. If two pages have similar images they can be assumed to be similar.
Usability Studies: Usability studies need to be done to find out the effectiveness of the focus+context views and the overview diagrams based on the landmark nodes. The visualizations could be improved based on user feedbacks. 8 ACKNOWLEDGEMENTS
We would like to thank the College of Computing, Georgia Tech for referring to information on their World-Wide Web server. We are also grateful to Dr. Jim Foley and Dr. Scott Hudson for their very useful comments. REFERENCES
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