Link Analysis

Link Analysis

• Human knowledge is real, convincing and trustable information

• Hyperlinks contain information about the human judgment

• Social sciences

  • Nodes (persons, organizations)

  • Edges (social interaction)

• e.g. Impact factor in scientific literature

Early Approach

• Basic Assumptions

  • Hyperlinks contain information about the human judgment of a site

  • The more incoming links to a site, the more it is important

• Bray (1996)

  • visibility : number of other sites pointing to it (in-degree)

  • luminosity : number of other sites to which it points (out-degree)

  • Limitation

    • failure to capture the relative importance of different parents (children) sites

• Mark

  • To calculate the score S of a document at vertex v

  • Limitation

    • require DAG, Unreasonable

• Marchiori

  • Hyper information should complement textual information to obtain the overall information

  • Limitation

    • Can’t handle real world cases

HITS

• Hypertext Induced Topic Selection

• For each vertex v in a subgraph of interest

  $$\begin{aligned} a(v) &= \text{the authority of } v \\ h(v) &= \text{the hubness of } v \end{aligned}$$

  • A site is very authoritative if it receives many citations

    • citation from important sites weight more.

  • Hubness shows the importance of a site

    • good hub links to many authoritative sites.

Approach

1. rank pages authority according to their in-degree
2. extend the root set to base set

• Authority and Hubness will converge

  • mutually reinforcing relationship

    $$a(v) := \sum_{w \in pa[v]} h(w) \\ h(v) := \sum_{w \in ch[v]} a(w)$$
• Let A denote the adjacency matrix of graph

  $$\begin{aligned} a_t &:= A^t h_{t-1} \\ h_t &:= A a_{t-1} \end{aligned}$$

Examples

initial all hubness and authority (h, a) to (1, 1)
loop:
    calculate hubness by children counts
    calculate authority by parent counts
    normalization:
        new h = node's hub / all hub
        new a = node's authority / all authorities

• The sites come from high hubness sites will have high authority

• The sites have high hubness will point to many high authority sites

Issues

• Mutually reinforcing relationships between hosts

  • Nepotistic links : 互相幫忙 cite

  • Link normalization

• Will be solved by improvements

  • The document can contain many identical links to the same document in another host

  • Links are generated automatically

  • Non-relevant Nodes

Observation

• The process of link analysis

  • Convergence of values of hubs and authorities

  • Two (hub, authority) pairs

Improvements

• Bharat and Henzinger : Combining Connectivity and Content Analysis

  • Assign weight to identical multiple edges

    • which are inversely proportional to their multiplicity

  • Prune irrelevant nodes or regulating the influence of a node with a relevance weight

PageRank

• The weight is assigned by the rank of parents

  • like pipe

    $$r(v) = \alpha \sum_{w \in pa[v]} \frac{r(w)}{\lvert ch[w]\rvert}$$
• No more hubness & authority weights, only parent rank

• Page rank is propotional to its parent's rank

  • Inversely propotional to its parent's out-degree

• Query Independent

Examples

PR	ID	OutLink	InLink
0.304	1	2,3,4,5,7	2,3,5,6
0.179	5	1,3,4,6	1,4,6,7
0.166	2	1	1,3,4
0.141	3	1,2	1,4,5
0.105	4	2,3,5	1,5
0.061	7	5	1
0.045	6	1,5	5

• PR value may proportional to in-degrees

• High PR value from high parent's PR value (2, 3, 4, 5, 7 from 1)

• Quick reference

  $$\begin{aligned} PR(P_i) &= \frac{d}{n} + (1-d) \times \sum_{l_{j,i}\in E} PR(P_j) / \text{Outdegree}(P_j) \\ \text{D(damping factor)} &= 0.1 - 0.15 \\ n &= \lvert \text{page set}\rvert \end{aligned}$$

Stability

• Whether the link analysis algorithms based on eigenvectors are stable in the sense that results don’t change significantly?

  • The connectivity of a portion of the graph is changed arbitrary

  • How will it affect the results of algorithms?

SALSA

• Probabilistic extension of the HITS algorithm

  • You can view it as HITS with normalization

• Random walk both in the forward and backward direction

• Two separate random walks

  • Hub walk

    • a forward link from $u_h$ to $w_a$

    • immediately a backward link from $w_a$ to $v_h$

      $$(u, w) \text{ and } (v, w) \in E$$
  • Authority walk

    • a backward link from $w_a$ to $u_h$

    • immediately a forward link from $u_h$ to $x_a$

      $$(u, w) \text{ and } (u, x) \in E$$

Limit of Link Analysis

• META tags/ invisible text

  • Search engines relying on meta tags

  • often misled (intentionally) by web developers

• Pay-for-place

  • organizations pay search engines and page rank

  • organizations pay high ranking pages for advertising space

• Stability

  • Adding even a small number of nodes/edges to the graph has a significant impact

• Topic drift

  • A top authority may be a hub of pages on a different topic

  • resulting in increased rank of the authority page

• Content evolution

  • Adding/removing links/content

  • affect the intuitive authority rank of a page requiring recalculation of page ranks

  • Incremental link analysis

Similarity measurement by links

• How similar two objects are within a network

  • How to measure the similarity between two objects based on links relationship

• Based on linked-structure

  • object-to-object relations

• Based on textual content

  • keywords co-currency

• Linked-based produce systematically better correlation with human judgements compared to the text-based one

SimRank

• Idea : Random Surfer model

• Two objects are similar

  • If they are linked with the same or similar objects

  • Consider : Inlink relationship

• Defined by recursively and computed by iteratively

$$S(a,b) = \frac{C}{\lvert I(a) \rvert \lvert I(b)\rvert} \sum_{i=1}^{\lvert I(a)\rvert}\sum_{j=1}^{\lvert I(b)\rvert} S(I_i(a), I_j(b))$$

• I(a), I(b) : all in-neighbors

• C : decay factor, between 0, 1

• S(a, b) between 0, 1

• S(a, a) = 1

Link analysis in a social network

• Node : entity

• Edge : relationship

• We want to know in this social network

  • which nodes/edges are influential/important

  • which node is an outlier

Centrality

• Degree centrality

  • In-degree, out-degree

• Closeness centrality

  • Geodesic distance between the entity and all other entities

• Betweeness centrality

  • Geodesic path

• Eigenvector centrality

  • Central entity receiving many communications from other wellconnected entities

• Power centrality

$$NET_\text{degree} = \frac{\sum_{v\in V}\text{Max}_{v\in V}\text{Degree}(v) - \text{Degree}(v)}{(n-1)\times (n-2)}$$

Conductance and NCPP

• Look at best community (2/8)

• Numerator means the community has 2 out-degree

• Denominator means the community's inner degree is 8

• NCPP uses conductance to find the best divide of communities