A near-optimal approach to edge connectivity-based hierarchical graph decomposition
Authors: Chang Lijun , Wang Zhiyi 
Published: May 2023

AbstractThe problem of efficiently computing all $$k$$
k
-edge-connected components ($$k$$
k
-ECCs) of a graph G for a user-givenk has been extensively studied recently in view of its importance in many applications. The $$k$$
k
-ECCs of G for all possible values ofk form a hierarchical structure; that is, any two different $$k$$
k
-ECCs for the same k value are disjoint and any $$k$$
k
-ECC is contained in a unique $$(k\text {-}1)$$

(
k
-
1
)

-ECC. In this paper, we study the problem of efficiently constructing the hierarchy tree of the $$k$$
k
-ECCs for all possible k values, for a graph G. The existing approaches $$\textsf{TD}$$
TD
and $$\textsf{BU}$$
BU
construct the hierarchy tree in either a top-down manner or a bottom-up manner, with both having the time complexity of $${{\mathcal {O}}}\big (\delta (G)\times {\mathsf {T_{KECC}}} (G)\big )$$

O

(

δ

(
G
)

×

T
KECC

(
G
)

)

, where $$\delta (G)$$

δ
(
G
)

is the degeneracy of G and $${\mathsf {T_{KECC}}} (G)$$

T
KECC

(
G
)

is the time complexity of computing all $$k$$
k
-ECCs of G for a specific k value. Here, the degeneracy of G is defined as the maximum value among the minimum vertex degrees of all subgraphs of G and is at most $$\sqrt{m}$$

m

where m is the number of edges in G. To improve the time complexity, we propose a divide-and-conquer approach $$\textsf{DC}$$
DC
running in $${{\mathcal {O}}}\big ( (\log \delta (G))\times {\mathsf {T_{KECC}}} (G)\big )$$

O

(

(
log
δ

(
G
)

)

×

T
KECC

(
G
)

)

time; this time complexity is optimal up to a logarithmic factor. However, a straightforward implementation of $$\textsf{DC}$$
DC
would take $${{\mathcal {O}}}( (m + n) \log \delta (G))$$

O
(
(
m
+
n
)
log
δ
(
G
)
)

main-memory space, which could easily run out-of-memory when processing large graphs; here, n is the number of vertices in G. To reduce the main-memory footprint of our algorithm, we propose adjacency array-based techniques to optimize the space complexity to $$2m+{{\mathcal {O}}}(n\log \delta (G))$$

2
m
+
O
(
n
log
δ
(
G
)
)

and denote our resulting algorithm by $$\mathsf {DC\text {-}AA}$$

DC
-
AA

. As a by-product of $$\mathsf {DC\text {-}AA}$$

DC
-
AA

, we also improve the space complexity of the state-of-the-art algorithm for computing all $$k$$
k
-ECCs for a specific k to $$2m + {{\mathcal {O}}}(n)$$

2
m
+
O
(
n
)

, by using the same technique as used in $$\mathsf {DC\text {-}AA}$$

DC
-
AA

. Finally, we propose optimization techniques to improve the practical efficiency of the existing approach $$\textsf{BU}$$
BU
and denote the space-optimized version of it as $$\mathsf {BU^*\text {-}AA}$$

BU
∗

-
AA

which runs in $${{\mathcal {O}}}\big (\delta (G)\times {\mathsf {T_{KECC}}} (G)\big )$$

O

(

δ

(
G
)

×

T
KECC

(
G
)

)

time and $$2m+{{\mathcal {O}}}(n)$$

2
m
+
O
(
n
)

space. Extensive experiments on large real graphs and synthetic graphs demonstrate that our algorithms $$\mathsf {DC\text {-}AA}$$

DC
-
AA

and $$\mathsf {BU^*\text {-}AA}$$

BU
∗

-
AA

outperform the state-of-the-art approaches by up to 28 times in terms of running time and by up to 8 times in terms of main memory usage. In particular, our approach $$\mathsf {BU^*\text {-}AA}$$

BU
∗

-
AA

processes the Twitter graph, which has more than 1 billion undirected edges, in 29 min with 13.5 GB memory, while the state-of-the-art approaches take more than 13 h after our space optimization; note that the state-of-the-art approaches run out-of-memory if without our space optimization. Our empirical study also shows that $$\mathsf {BU^*\text {-}AA}$$

BU
∗

-
AA

, despite having a higher time complexity, performs better than $$\mathsf {DC\text {-}AA}$$

DC
-
AA

in practice. We also remark that $$\mathsf {BU^*\text {-}AA}$$

BU
∗

-
AA

is much simpler and easier to implement than $$\mathsf {DC\text {-}AA}$$

DC
-
AA

.